-deprecated\").","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"","code":"plot.mixedAn(x, y.limits=NULL, pos=c(0,1), graph=c(\"base\",\"ggplot2\"),...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"x object class mixedAn, given output call function mixedAn. y.limits Range y-axis graph. default value NULL, case maximum range optimal mixed analysis scenarios considered. pos Parameter set position legend. Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. Default value c(0,1), topleft corner inside plot area. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods, graphical parameters (see par).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"evi ggplot object containing plot. Returned graph=\"ggplot2\". function produces graph showing difference ''optimal'' version EVPI (cost-effective intervention included market) mixed strategy one (one intervention considered market).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"plot-mixedan","dir":"Reference","previous_headings":"","what":"plot.mixedAn","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"plot.mixedAn, use evi.plot. Summary plot health economic analysis mixed analysis considered Compares optimal scenario mixed case terms EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-package.html","id":null,"dir":"Reference","previous_headings":"","what":"BCEA: Bayesian Cost Effectiveness Analysis — BCEA-package","title":"BCEA: Bayesian Cost Effectiveness Analysis — BCEA-package","text":"Produces economic evaluation sample suitable variables cost effectiveness / utility two interventions, e.g. Bayesian model form MCMC simulations. package computes cost-effective alternative produces graphical summaries probabilistic sensitivity analysis, see Baio et al (2017) doi:10.1007/978-3-319-55718-2 .","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BCEA: Bayesian Cost Effectiveness Analysis — BCEA-package","text":"Maintainer: Gianluca Baio g.baio@ucl.ac.uk (ORCID) [copyright holder] Authors: Andrea Berardi .berardi@ucl.ac.uk (ORCID) Anna Heath anna.heath@sickkids.ca (ORCID) Nathan Green n.green@ucl.ac.uk (ORCID)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"Cost-effectiveness analysis based results simulation model variable clinical benefits (e) costs (c). Produces results post-processed give health economic analysis. output stored object class \"bcea\".","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"","code":"bcea( eff, cost, ref = 1, interventions = NULL, .comparison = NULL, Kmax = 50000, k = NULL, plot = FALSE, ... ) # S3 method for default bcea( eff, cost, ref = NULL, interventions = NULL, .comparison = NULL, Kmax = 50000, k = NULL, plot = FALSE, ... ) # S3 method for rjags bcea(eff, ...) # S3 method for rstan bcea(eff, ...) # S3 method for bugs bcea(eff, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"eff object containing nsim simulations variable clinical effectiveness intervention considered. general matrix nsim rows nint columns. partially matched `e' previous version `BCEA` back-compatibility. cost object containing nsim simulations variable cost intervention considered. general matrix nsim rows nint columns. partially matched `c' previous version `BCEA` back-compatibility. ref Defines intervention (columns eff cost) considered reference strategy. default value ref = 1 means intervention associated first column eff cost reference one(s) associated column(s) () comparators. interventions Defines labels associated intervention. default NULL, assigns labels form \"Intervention1\", ... , \"InterventionT\". .comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison = 2). Kmax Maximum value willingness pay considered. Default value k = 50000. willingness pay approximated discrete grid interval [0, Kmax]. grid equal k parameter given, composed 501 elements k = NULL (default). k (n optional) vector values willingness pay grid. length > 1 otherwise plots empty. specified BCEA construct grid 501 values 0 Kmax. option useful performing intensive computations (e.g. EVPPI). changed wtp previous versions consistency functions deprecated future. plot logical value indicating whether function produce summary plot . ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"object class \"bcea\" containing following elements n_sim Number simulations produced Bayesian model n_comparators Number interventions analysed n_comparisons Number possible pairwise comparisons delta.e possible comparison, differential effectiveness measure delta.c possible comparison, differential cost measure ICER value Incremental Cost-Effectiveness Ratio Kmax maximum value assumed willingness pay threshold k vector values grid approximation willingness pay ceac value Cost-Effectiveness Acceptability Curve, function willingness pay ib distribution Incremental Benefit, given willingness pay eib value Expected Incremental Benefit, function willingness pay kstar grid approximation break-even point(s) best vector containing numeric label intervention cost-effective value willingness pay selected grid approximation U array including value expected utility simulation Bayesian model, value grid approximation willingness pay intervention considered vi array including value information simulation Bayesian model value grid approximation willingness pay Ustar array including maximum \"known-distribution\" utility simulation Bayesian model value grid approximation willingness pay ol array including opportunity loss simulation Bayesian model value grid approximation willingness pay evi vector values Expected Value Information, function willingness pay interventions vector labels interventions considered ref numeric index associated intervention used reference analysis comp numeric index(es) associated intervention(s) used comparator(s) analysis step step size used form grid approximation willingness pay e eff matrix used generate object (see Arguments) c cost matrix used generate object (see Arguments)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"Baio G (2013). Bayesian Methods Health Economics. CRC. Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"Gianluca Baio, Andrea Berardi, Nathan Green","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"","code":"# See Baio (2013), Baio (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea( e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) plot=TRUE # plots the results ) # Creates a summary table summary( m, # uses the results of the economic evaluation # (a \"bcea\" object) wtp=25000 # selects the particular value for k ) #> #> Cost-effectiveness analysis summary #> #> Reference intervention: Vaccination #> Comparator intervention: Status Quo #> #> Optimal decision: choose Status Quo for k < 20100 and Vaccination for k >= 20100 #> #> #> Analysis for willingness to pay parameter k = 25000 #> #> Expected net benefit #> Status Quo -36.054 #> Vaccination -34.826 #> #> EIB CEAC ICER #> Vaccination vs Status Quo 1.2284 0.529 20098 #> #> Optimal intervention (max expected net benefit) for k = 25000: Vaccination #> #> EVPI 2.4145 # \\donttest{ # Plots the cost-effectiveness plane using base graphics ceplane.plot( m, # plots the Cost-Effectiveness plane comparison=1, # if more than 2 interventions, selects the # pairwise comparison wtp=25000, # selects the relevant willingness to pay # (default: 25,000) graph=\"base\" # selects base graphics (default) ) # Plots the cost-effectiveness plane using ggplot2 if (requireNamespace(\"ggplot2\")) { ceplane.plot( m, # plots the Cost-Effectiveness plane comparison=1, # if more than 2 interventions, selects the # pairwise comparison wtp=25000, # selects the relevant willingness to pay # (default: 25,000) graph=\"ggplot2\"# selects ggplot2 as the graphical engine ) # Some more options ceplane.plot( m, graph=\"ggplot2\", pos=\"top\", size=5, ICER_size=1.5, label.pos=FALSE, opt.theme=ggplot2::theme(text=ggplot2::element_text(size=8)) ) } # Plots the contour and scatterplot of the bivariate # distribution of (Delta_e,Delta_c) contour( m, # uses the results of the economic evaluation # (a \"bcea\" object) comparison=1, # if more than 2 interventions, selects the # pairwise comparison nlevels=4, # selects the number of levels to be # plotted (default=4) levels=NULL, # specifies the actual levels to be plotted # (default=NULL, so that R will decide) scale=0.5, # scales the bandwidths for both x- and # y-axis (default=0.5) graph=\"base\" # uses base graphics to produce the plot ) # Plots the contour and scatterplot of the bivariate # distribution of (Delta_e,Delta_c) contour2( m, # uses the results of the economic evaluation # (a \"bcea\" object) wtp=25000, # selects the willingness-to-pay threshold ) # Using ggplot2 if (requireNamespace(\"ggplot2\")) { contour2( m, # uses the results of the economic evaluation # (a \"bcea\" object) graph=\"ggplot2\",# selects the graphical engine wtp=25000, # selects the willingness-to-pay threshold label.pos=FALSE # alternative position for the wtp label ) } # Plots the Expected Incremental Benefit for the \"bcea\" object m eib.plot(m) # Plots the distribution of the Incremental Benefit ib.plot( m, # uses the results of the economic evaluation # (a \"bcea\" object) comparison=1, # if more than 2 interventions, selects the # pairwise comparison wtp=25000, # selects the relevant willingness # to pay (default: 25,000) graph=\"base\" # uses base graphics ) # Produces a plot of the CEAC against a grid of values for the # willingness to pay threshold ceac.plot(m) # Plots the Expected Value of Information for the \"bcea\" object m evi.plot(m) # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimal intervention — best_interv_given_k","title":"Optimal intervention — best_interv_given_k","text":"Select best option value willingness pay.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimal intervention — best_interv_given_k","text":"","code":"best_interv_given_k(eib, ref, comp)"},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimal intervention — best_interv_given_k","text":"eib Expected incremental benefit ref Reference group number comp Comparison group number(s)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimal intervention — best_interv_given_k","text":"Group index","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"Produces plot Cost-Effectiveness Acceptability Curve (CEAC) willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"","code":"# S3 method for bcea ceac.plot( he, comparison = NULL, pos = c(1, 0), graph = c(\"base\", \"ggplot2\", \"plotly\"), ... ) ceac.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". plotting functions \"plotly\" implementation yet. ... graph = \"ggplot2\" named theme object supplied, passed ggplot2 object. usual ggplot2 syntax used. Additional arguments: line = list(color): specifies line colour(s) - graph types. line = list(type): specifies line type(s) lty numeric values - graph types. line = list(size): specifies line width(s) numeric values - graph types. currency: Currency prefix willingness pay values - ggplot2 . area_include: logical, include area CEAC curves - plotly . area_color: specifies AUC colour - plotly .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"ceac graph = \"ggplot2\" ggplot object, graph = \"plotly\" plotly object containing requested plot. Nothing returned graph = \"base\", default. function produces plot cost-effectiveness acceptability curve discrete grid possible values willingness pay parameter. Values CEAC closer 1 indicate uncertainty cost-effectiveness reference intervention low. Similarly, values CEAC closer 0 indicate uncertainty cost-effectiveness comparator low.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"CEAC estimates probability cost-effectiveness, respect given willingness pay threshold. CEAC used mainly evaluate uncertainty associated decision-making process, since enables quantification preference compared interventions, defined terms difference utilities. Formally, CEAC defined : $$\\textrm{CEAC} = P(\\textrm{IB}(\\theta) > 0)$$ net benefit function used utility function, definition can re-written $$\\textrm{CEAC} = P(k \\cdot \\Delta_e - \\Delta_c > 0)$$ effectively depending willingness pay value \\(k\\).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"","code":"data(\"Vaccine\") he <- BCEA::bcea(eff, cost) #> No reference selected. 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ceac_plot_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot By Graph Device — ceac_plot_graph","text":"","code":"ceac_plot_base(he, pos_legend, graph_params, ...) # S3 method for pairwise ceac_plot_base(he, pos_legend, graph_params, ...) # S3 method for bcea ceac_plot_base(he, pos_legend, graph_params, ...) ceac_plot_ggplot(he, pos_legend, graph_params, ...) # S3 method for pairwise ceac_plot_ggplot(he, pos_legend, graph_params, ...) # S3 method for bcea ceac_plot_ggplot(he, pos_legend, graph_params, ...) ceac_ggplot(he, pos_legend, graph_params, ceac, ...) ceac_plot_plotly(he, pos_legend = \"left\", graph_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot By Graph Device — ceac_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. pos_legend Legend position graph_params Aesthetic ggplot parameters ... Additional arguments ceac ceac index ","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"Produces plot Cost-Effectiveness Acceptability Frontier (CEAF) willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"","code":"# S3 method for pairwise ceaf.plot(mce, graph = c(\"base\", \"ggplot2\"), ...) ceaf.plot(mce, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"mce output call function multi.ce graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"ceaf ggplot object containing plot. Returned graph=\"ggplot2\".","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea( e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) plot=FALSE # inhibits graphical output ) # \\donttest{ mce <- multi.ce(m) # uses the results of the economic analysis # } # \\donttest{ ceaf.plot(mce) # plots the CEAF # } # \\donttest{ ceaf.plot(mce, graph = \"g\") # uses ggplot2 # } # \\donttest{ # Use the smoking cessation dataset data(Smoking) m <- bcea(eff, cost, ref = 4, intervention = treats, Kmax = 500, plot = FALSE) mce <- multi.ce(m) ceaf.plot(mce) # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"line connecting successive points cost-effectiveness plane represent effect cost associated different treatment alternatives. gradient line segment represents ICER treatment comparison two alternatives represented segment. cost-effectiveness frontier consists set points corresponding treatment alternatives considered cost-effective different values cost-effectiveness threshold. steeper gradient successive points frontier, higher ICER treatment alternatives expensive alternative considered cost-effective high value cost-effectiveness threshold assumed. Points lying cost-effectiveness frontier represent treatment alternatives considered cost-effective value cost-effectiveness threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"","code":"# S3 method for bcea ceef.plot( he, comparators = NULL, pos = c(1, 1), start.from.origins = TRUE, threshold = NULL, flip = FALSE, dominance = TRUE, relative = FALSE, print.summary = TRUE, graph = c(\"base\", \"ggplot2\"), print.plot = TRUE, ... ) ceef.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparators Vector specifying comparators included frontier analysis. must length > 1. Default NULL includes available comparators. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. start..origins Logical. frontier start origins axes? argument reset FALSE average effectiveness /costs least one comparator negative. threshold Specifies efficiency defined based willingness--pay threshold value. set NULL (default), conditions included slope increase. positive value passed argument, efficient intervention also requires ICER comparison versus last efficient strategy greater specified threshold value. negative value ignored warning. flip Logical. axes plane inverted? dominance Logical. dominance regions included plot? relative Logical. plot display absolute measures (default FALSE) differential outcomes versus reference comparator? print.summary Logical. efficiency frontier summary printed along graph? See Details additional information. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". print.plot Logical. efficiency frontier plotted? ... graph_type=\"ggplot2\" named theme object supplied, added ggplot object. Ignored graph_type=\"base\". Setting optional argument include.ICER TRUE print ICERs summary tables, produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"ceplane ggplot object containing plot. Returned graph_type=\"ggplot2\". function produces plot cost-effectiveness efficiency frontier. dots show simulated values intervention-specific distributions effectiveness costs. circles indicate average bivariate distribution, numbers referring included intervention. numbers inside circles black intervention included frontier grey otherwise. option dominance set TRUE, dominance regions plotted, indicating areas dominance. Interventions areas dominance region frontier situation extended dominance.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"Back compatibility BCEA previous versions: bcea objects include generating e c matrices BCEA versions <2.1-0. function compatible objects created previous versions. matrices can appended bcea objects obtained using previous versions, making sure class object remains unaltered. argument print.summary allows printing brief summary efficiency frontier, default TRUE. Two tables plotted, one interventions included frontier one dominated interventions. average costs clinical benefits included intervention. frontier table includes slope increase frontier non-frontier table displays dominance type dominated intervention. Please note slopes defined increment costs unit increment benefits even flip = TRUE consistency ICER definition. angle increase radians depends definition axes, .e. value given flip argument. argument relative set TRUE, graph display absolute measures costs benefits. Instead axes represent differential costs benefits compared reference intervention (indexed ref bcea function).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"Baio G (2013). Bayesian Methods Health Economics. CRC. IQWIG (2009). “General Methods Assessment Relation Benefits Cost, Version 1.0.” Institute Quality Efficiency Health Care (IQWIG).","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"Andrea Berardi, Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"","code":"## create the bcea object m for the smoking cessation example data(Smoking) m <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats) ## produce plot ceef.plot(m, graph = \"base\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.28824 45.733 158.66 1.5645 #> Group counselling 0.72252 143.301 224.67 1.5663 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.00000 0.000 Extended dominance #> Individual counselling 0.48486 94.919 Extended dominance # \\donttest{ ## tweak the options ## flip axis ceef.plot(m, flip = TRUE, dominance = FALSE, start.from.origins = FALSE, print.summary = FALSE, graph = \"base\") ## or use ggplot2 instead if(require(ggplot2)){ ceef.plot(m, dominance = TRUE, start.from.origins = FALSE, pos = TRUE, print.summary = FALSE, graph = \"ggplot2\") } #> Loading required package: ggplot2 #> Warning: package 'ggplot2' was built under R version 4.2.3 # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary table for CEEF — ceef.summary","title":"Summary table for CEEF — ceef.summary","text":"Summary table CEEF","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary table for CEEF — ceef.summary","text":"","code":"ceef.summary(he, frontier_data, frontier_params, include.ICER = FALSE, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary table for CEEF — ceef.summary","text":"bcea object containing results Bayesian modelling economic evaluation. frontier_data Frontier data frontier_params Frontier parameters include.ICER include ICER? default: FALSE ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary table for CEEF — ceef.summary","text":"Summary printed console","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Efficiency Frontier Plot By Graph Device — ceef_plot_graph","title":"Cost-effectiveness Efficiency Frontier Plot By Graph Device — ceef_plot_graph","text":"Choice base R, ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Efficiency Frontier Plot By Graph Device — ceef_plot_graph","text":"","code":"ceef_plot_ggplot(he, frontier_data, frontier_params, ...) ceef_plot_base(he, frontier_data, frontier_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Efficiency Frontier Plot By Graph Device — ceef_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. frontier_data Frontier data frontier_params Frontier parameters ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"Produces scatter plot cost-effectiveness plane, together sustainability area, function selected willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"","code":"# S3 method for bcea ceplane.plot( he, comparison = NULL, wtp = 25000, pos = c(0, 1), graph = c(\"base\", \"ggplot2\", \"plotly\"), ... ) ceplane.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison = c(1,3) comparison = 2). wtp value willingness pay parameter. used graph = \"base\" multiple comparisons. pos Parameter set position legend; single comparison plot, ICER legend position. Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. Default value c(1,1), topright corner inside plot area. graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". ... graph = \"ggplot2\" named theme object supplied, passed ggplot2 object. usual ggplot2 syntax used. Additional graphical arguments: label.pos = FALSE: place willingness pay label different position bottom graph - base ggplot2 ( label plotly). line = list(color): colour specifying colour willingness--pay line. point = list(color): vector colours specifying colour(s) associated cloud points. length 1 equal number comparisons. point = list(size): vector colours specifying size(s) points. length 1 equal number comparisons. point = list(shape): vector shapes specifying type(s) points. length 1 equal number comparisons. icer = list(color): vector colours specifying colour(s) ICER points. length 1 equal number comparisons. icer = list(size): vector colours specifying size(s) ICER points. length 1 equal number comparisons. area_include: logical, include exclude cost-effectiveness acceptability area (default TRUE). area = list(color): colour specifying colour cost-effectiveness acceptability area. currency: Currency prefix cost differential values - ggplot2 . icer_annot: Annotate ICER point text label - ggplot2 .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"graph = \"ggplot2\" ggplot object, graph = \"plotly\" plotly object containing requested plot. Nothing returned graph = \"base\", default. Grey dots show simulated values joint distribution effectiveness cost differentials. larger red dot shows ICER grey area identifies sustainability area, .e. part plan simulated values willingness pay threshold. proportion points sustainability area effectively represents CEAC given value willingness pay. comparators 2 pairwise comparison specified, scatterplots graphed using different colours.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"plotly version, point_colors, ICER_colors area_color can also specified rgba colours using either [plotly]toRGB function rgba colour string, e.g. 'rgba(1, 1, 1, 1)'.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"","code":"## create the bcea object for the smoking cessation example data(Smoking) m <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats) ## produce the base plot ceplane.plot(m, wtp = 200, graph = \"base\") ## select only one comparator ceplane.plot(m, wtp = 200, graph = \"base\", comparison = 3) ## use ggplot2 if (requireNamespace(\"ggplot2\")) { ceplane.plot(m, wtp = 200, pos = \"right\", icer = list(size = 2), graph = \"ggplot2\") } ## plotly ceplane.plot(m, wtp = 200, graph = 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Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"","code":"# S3 method for bcea ceplane_plot_base(he, wtp = 25000, pos_legend, graph_params, ...) ceplane_plot_base(he, ...) # S3 method for bcea ceplane_plot_ggplot(he, wtp = 25000, pos_legend, graph_params, ...) ceplane_plot_ggplot(he, ...) # S3 method for bcea ceplane_plot_plotly(he, wtp = 25000, pos_legend, graph_params, ...) ceplane_plot_plotly(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. wtp Willingness pay threshold; default 25,000 pos_legend Legend position graph_params Graph parameters ggplot2 format ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"base R returns plot ggplot2 returns ggplot2 object plotly returns plot Viewer","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"","code":"# single comparator data(Vaccine, package = \"BCEA\") he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ceplane.plot(he, graph = \"base\") if (FALSE) { # need to provide all the defaults because thats what # ceplane.plot() does graph_params <- list(xlab = \"x-axis label\", ylab = \"y-axis label\", title = \"my title\", xlim = c(-0.002, 0.001), ylim = c(-13, 5), point = list(sizes = 1, colors = \"darkgrey\"), area = list(color = \"lightgrey\")) he$delta_e <- as.matrix(he$delta_e) he$delta_c <- as.matrix(he$delta_c) BCEA::ceplane_plot_base(he, graph_params = graph_params) ## single non-default comparator ## multiple comparators data(Smoking) graph_params <- list(xlab = \"x-axis label\", ylab = \"y-axis label\", title = \"my title\", xlim = c(-1, 2.5), ylim = c(-1, 160), point = list(sizes = 0.5, colors = grey.colors(3, start = 0.1, end = 0.7)), area = list(color = \"lightgrey\")) he <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats) BCEA::ceplane_plot_base(he, wtp = 200, pos_legend = FALSE, graph_params = graph_params) } data(Vaccine) he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ceplane.plot(he, graph = \"ggplot2\") ceplane.plot(he, wtp=10000, graph = \"ggplot2\", point = list(colors = \"blue\", sizes = 2), area = list(col = \"springgreen3\")) data(Smoking) he <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats) ceplane.plot(he, graph = \"ggplot2\") ceplane.plot(he, wtp = 200, pos = \"right\", ICER_size = 2, graph = \"ggplot2\") ceplane.plot(he, wtp = 200, pos = TRUE, graph = \"ggplot2\") ceplane.plot(he, graph = \"ggplot2\", wtp=200, theme = ggplot2::theme_linedraw())"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"Extends standard cost-effectiveness analysis modify utility function risk aversion decision maker explicitly accounted . Default vector risk aversion parameters: 1e-11, 2.5e-6, 5e-6","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"","code":"CEriskav(he) <- value # S3 method for bcea CEriskav(he) <- value # S3 method for default CEriskav(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value vector values risk aversion parameter. NULL, default values assigned R. first (smallest) value (r -> 0) produces standard analysis risk aversion.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"object class CEriskav containing following elements: Ur array containing simulated values ''known-distribution'' utilities interventions, values willingness pay parameter possible values r Urstar array containing simulated values maximum ''known-distribution'' expected utility values willingness pay parameter possible values r IBr array containing simulated values distribution Incremental Benefit values willingness pay possible values r eibr array containing Expected Incremental Benefit value willingness pay parameter possible values r vir array containing simulations Value Information value willingness pay parameter possible values r evir array containing Expected Value Information value willingness pay parameter possible values r R number possible values parameter risk aversion r r vector containing possible values parameter risk aversion r","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff,c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000 # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) ) # Define the vector of values for the risk aversion parameter, r, eg: r <- c(1e-10, 0.005, 0.020, 0.035) # Run the cost-effectiveness analysis accounting for risk aversion # \\donttest{ # uses the results of the economic evaluation # if more than 2 interventions, selects the # pairwise comparison CEriskav(m) <- r # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Plot Including a Parameter of Risk Aversion — CEriskav_plot_graph","title":"Cost-effectiveness Plot Including a Parameter of Risk Aversion — CEriskav_plot_graph","text":"Choice base R, ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Plot Including a Parameter of Risk Aversion — CEriskav_plot_graph","text":"","code":"CEriskav_plot_base(he, pos_legend) CEriskav_plot_ggplot(he, pos_legend)"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Plot Including a Parameter of Risk Aversion — CEriskav_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. pos_legend Legend position","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness summary statistics table — ce_table","title":"Cost-effectiveness summary statistics table — ce_table","text":"commonly shown journal paper.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness summary statistics table — ce_table","text":"","code":"ce_table(he, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness summary statistics table — ce_table","text":"bcea object containing results Bayesian modelling economic evaluation. wtp Willingness pay ... Additional parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness summary statistics table — ce_table","text":"","code":"data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea( e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) ) ce_table(m) #> cost eff delta.c delta.e ICER INB #> Vaccination 14.691446 -0.000805370 NA NA NA NA #> Status Quo 9.655464 -0.001055946 5.035983 0.0002505764 20097.59 1.228428"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute EVPPI — compute.evppi","title":"Compute EVPPI — compute.evppi","text":"Compute EVPPI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute EVPPI — compute.evppi","text":"","code":"# S3 method for evppi compute(he, fit.full)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute EVPPI — compute.evppi","text":"bcea object containing results Bayesian modelling economic evaluation. fit.full fit.full","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute EVPPI — compute.evppi","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","title":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","text":"Compute Cost-Effectiveness Acceptability Curve","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","text":"","code":"compute_CEAC(ib)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","text":"ib Incremental benefit","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","text":"Array dimensions (interv x k)","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ceaf.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Cost-Effectiveness Acceptability Frontier — compute_ceaf","title":"Compute Cost-Effectiveness Acceptability Frontier — compute_ceaf","text":"Compute Cost-Effectiveness Acceptability Frontier","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ceaf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Cost-Effectiveness Acceptability Frontier — compute_ceaf","text":"","code":"compute_ceaf(p_best_interv)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ceaf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Cost-Effectiveness Acceptability Frontier — compute_ceaf","text":"p_best_interv Probability best intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Expected Incremental Benefit — compute_EIB","title":"Compute Expected Incremental Benefit — compute_EIB","text":"summary measure useful assess potential changes decision different scenarios.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Expected Incremental Benefit — compute_EIB","text":"","code":"compute_EIB(ib)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Expected Incremental Benefit — compute_EIB","text":"ib Incremental benefit","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Expected Incremental Benefit — compute_EIB","text":"Array dimensions (interv x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Expected Incremental Benefit — compute_EIB","text":"considering pairwise comparison (e.g. simple case reference intervention \\(t = 1\\) comparator, status quo, \\(t = 0\\)), defined difference expected utilities two alternatives: $$eib := \\mbox{E}[u(e,c;1)] - \\mbox{E}[u(e,c;0)] = \\mathcal{U}^1 - \\mathcal{U}^0.$$ Analysis expected incremental benefit describes decision changes different values threshold. EIB marginalises uncertainty, incorporate describe explicitly uncertainty outcomes. overcome problem tool choice CEAC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Credible Intervals — compute_eib_cri","title":"Calculate Credible Intervals — compute_eib_cri","text":"expected incremental benefit plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Credible Intervals — compute_eib_cri","text":"","code":"compute_eib_cri(he, alpha_cri = 0.05, cri.quantile = TRUE)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Credible Intervals — compute_eib_cri","text":"bcea object containing results Bayesian modelling economic evaluation. alpha_cri Significance level, 0 - 1 cri.quantile Credible interval quantile?; logical","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Credible Intervals — compute_eib_cri","text":"cri","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Expected Value of Information — compute_EVI","title":"Compute Expected Value of Information — compute_EVI","text":"Compute Expected Value Information","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Expected Value of Information — compute_EVI","text":"","code":"compute_EVI(ol)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Expected Value of Information — compute_EVI","text":"ol Opportunity loss","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Expected Value of Information — compute_EVI","text":"EVI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute EVPPI — compute_evppi","title":"Compute EVPPI — compute_evppi","text":"Compute EVPPI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute EVPPI — compute_evppi","text":"","code":"compute_evppi(he, fit.full)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute EVPPI — compute_evppi","text":"bcea object containing results Bayesian modelling economic evaluation. fit.full fit.full","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute EVPPI — compute_evppi","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Incremental Benefit — compute_IB","title":"Compute Incremental Benefit — compute_IB","text":"Sample incremental net monetary benefit willingness--pay threshold, \\(k\\), comparator.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Incremental Benefit — compute_IB","text":"","code":"compute_IB(df_ce, k)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Incremental Benefit — compute_IB","text":"df_ce Dataframe cost effectiveness deltas k Vector willingness pay values","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Incremental Benefit — compute_IB","text":"Array dimensions (k x sim x ints)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Incremental Benefit — compute_IB","text":"Defined : $$IB = u(e,c; 1) - u(e,c; 0).$$ net benefit function used utility function, definition can re-written $$IB = k\\cdot\\Delta_e - \\Delta_c.$$","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"Defined ","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"","code":"compute_ICER(df_ce)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"df_ce Cost-effectiveness dataframe","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"ICER comparisons","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"$$ICER = \\Delta_c/\\Delta_e$$","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute k^* — compute_kstar","title":"Compute k^* — compute_kstar","text":"Find willingness--pay threshold optimal decision changes.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute k^* — compute_kstar","text":"","code":"compute_kstar(k, best, ref)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute k^* — compute_kstar","text":"k Willingness--pay grid approximation budget willing invest (vector) best Best intervention `k` (int) ref Reference intervention (int)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute k^* — compute_kstar","text":"integer representing intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute k^* — compute_kstar","text":"$$k^* := \\min\\{k : IB < 0 \\}$$ value break-even point corresponds ICER quantifies point decision-maker indifferent two options.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Opportunity Loss — compute_ol","title":"Compute Opportunity Loss — compute_ol","text":"difference maximum utility computed current parameter configuration (e.g. current simulation) \\(U^*\\) current utility intervention associated maximum utility overall.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Opportunity Loss — compute_ol","text":"","code":"compute_ol(Ustar, U, best)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Opportunity Loss — compute_ol","text":"Ustar Maximum utility value (sim x k) U Net monetary benefit (sim x k x interv) best Best intervention given willingness--pay (k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Opportunity Loss — compute_ol","text":"Array dimensions (sim x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Opportunity Loss — compute_ol","text":"mathematical notation, $$\\textrm{OL}(\\theta) := U^*(\\theta) - U(\\theta^\\tau)$$ \\(\\tau\\) intervention associated overall maximum utility \\(U^*(\\theta)\\) maximum utility value among comparators given simulation. opportunity loss non-negative quantity, since \\(U(\\theta^\\tau)\\leq U^*(\\theta)\\). simulations intervention cost-effective (.e. incremental benefit positive), \\(\\textrm{OL}(\\theta) = 0\\) opportunity loss, parameter configuration one obtained current simulation.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_p_best_interv.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Probability Best Intervention — compute_p_best_interv","title":"Compute Probability Best Intervention — compute_p_best_interv","text":"Compute Probability Best Intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_p_best_interv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Probability Best Intervention — compute_p_best_interv","text":"","code":"compute_p_best_interv(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_p_best_interv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Probability Best Intervention — compute_p_best_interv","text":"bcea object containing results Bayesian modelling economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute U Statistic — compute_U","title":"Compute U Statistic — compute_U","text":"Sample net (monetary) benefit willingness--pay threshold intervention.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute U Statistic — compute_U","text":"","code":"compute_U(df_ce, k)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute U Statistic — compute_U","text":"df_ce Cost-effectiveness dataframe k Willingness pay vector","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute U Statistic — compute_U","text":"Array dimensions (sim x k x ints)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ubar.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute NB for mixture of interventions — compute_Ubar","title":"Compute NB for mixture of interventions — compute_Ubar","text":"Compute NB mixture interventions","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ubar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute NB for mixture of interventions — compute_Ubar","text":"","code":"compute_Ubar(he, value)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ubar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute NB for mixture of interventions — compute_Ubar","text":"bcea object containing results Bayesian modelling economic evaluation. value Mixture weights","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Ustar Statistic — compute_Ustar","title":"Compute Ustar Statistic — compute_Ustar","text":"maximum utility value among comparators, indicating intervention produced benefits simulation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Ustar Statistic — compute_Ustar","text":"","code":"compute_Ustar(U)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Ustar Statistic — compute_Ustar","text":"U Net monetary benefit (sim x k x intervs)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Ustar Statistic — compute_Ustar","text":"Array dimensions (sim x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Value of Information — compute_vi","title":"Compute Value of Information — compute_vi","text":"difference maximum utility computed current parameter configuration \\(U^*\\) utility intervention associated maximum utility overall.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Value of Information — compute_vi","text":"","code":"compute_vi(Ustar, U)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Value of Information — compute_vi","text":"Ustar Maximum utility value (sim x k) U Net monetary benefit (sim x k x interv)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Value of Information — compute_vi","text":"Array dimensions (sim x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Value of Information — compute_vi","text":"value obtaining additional information parameter \\(\\theta\\) reduce uncertainty decisional process. defined : $$\\textrm{VI}(\\theta) := U^*(\\theta) - \\mathcal{U}^*$$ \\(U^*(\\theta)\\) maximum utility value given simulation among comparators \\(\\mathcal{U}^*(\\theta)\\) expected utility gained adoption cost-effective intervention.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/comp_names_from_.html","id":null,"dir":"Reference","previous_headings":"","what":"Comparison Names From — comp_names_from_","title":"Comparison Names From — comp_names_from_","text":"Comparison Names ","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/comp_names_from_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Comparison Names From — comp_names_from_","text":"","code":"comp_names_from_(df_ce)"},{"path":"https://n8thangreen.github.io/BCEA/reference/comp_names_from_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Comparison Names From — comp_names_from_","text":"df_ce Cost-effectiveness dataframe","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":null,"dir":"Reference","previous_headings":"","what":"Contour Plots for the Cost-Effectiveness Plane — contour.bcea","title":"Contour Plots for the Cost-Effectiveness Plane — contour.bcea","text":"Contour method objects class bcea. Produces scatterplot cost-effectiveness plane, contour-plot bivariate density differentials cost (y-axis) effectiveness (x-axis).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contour Plots for the Cost-Effectiveness Plane — contour.bcea","text":"","code":"# S3 method for bcea contour( he, pos = c(0, 1), graph = c(\"base\", \"ggplot2\"), comparison = NULL, ... ) contour(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contour Plots for the Cost-Effectiveness Plane — contour.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". plotting functions \"plotly\" implementation yet. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). ... Additional graphical arguments. usual ggplot2 syntax used regardless graph type. xlim: range plot along x-axis. NULL (default) determined range simulated values delta_e ylim: range plot along y-axis. NULL (default) determined range simulated values delta_c scale: Scales plot function observed standard deviation. levels: Numeric vector levels draw contour lines. Quantiles 0 [1] \"14 \\nLinear dependence: removing column pi.2.2.\" #> [2] \"15 \\nLinear dependence: removing column pi.2.2.\" #> [3] \"16 \\nLinear dependence: removing column pi.2.2.\" #> [4] \"17 \\nLinear dependence: removing column pi.2.2.\" #> [5] \"18 \\nLinear dependence: removing column pi.2.2.\" #> [6] \"19 \\nLinear dependence: removing column pi.2.2.\" #> [7] \"20 \\nLinear dependence: removing column pi.2.2.\" #> [8] \"21 \\nLinear dependence: removing column pi.2.2.\" #> [9] \"22 \\nLinear dependence: removing column pi.2.2.\" #> [10] \"29 \\nLinear dependence: removing column pi.2.2.\" #> [11] \"44 \\nLinear dependence: removing column pi.2.2.\" #> [12] \"45 \\nLinear dependence: removing column pi.2.2.\" #> [13] \"46 \\nLinear dependence: removing column pi.2.2.\" #> [14] \"47 \\nLinear dependence: removing column pi.2.2.\" #> [1] \"14 \\nLinear dependence: removing column pi.2.1.\" #> [2] \"15 \\nLinear dependence: removing column pi.2.1.\" #> [3] \"16 \\nLinear dependence: removing column pi.2.1.\" #> [4] \"17 \\nLinear dependence: removing column pi.2.1.\" #> [5] \"18 \\nLinear dependence: removing column pi.2.1.\" #> [6] \"19 \\nLinear dependence: removing column pi.2.1.\" #> [7] \"20 \\nLinear dependence: removing column pi.2.1.\" #> [8] \"21 \\nLinear dependence: removing column pi.2.1.\" #> [9] \"22 \\nLinear dependence: removing column pi.2.1.\" #> [10] \"29 \\nLinear dependence: removing column pi.2.1.\" #> [11] \"44 \\nLinear dependence: removing column pi.2.1.\" #> [12] \"45 \\nLinear dependence: removing column pi.2.1.\" #> [1] \"14 \\nLinear dependence: removing column pi.1.1.\" #> [2] \"15 \\nLinear dependence: removing column pi.1.1.\" #> [3] \"16 \\nLinear dependence: removing column pi.1.1.\" #> [4] \"17 \\nLinear dependence: removing column pi.1.1.\" #> [5] \"18 \\nLinear dependence: removing column pi.1.1.\" #> [6] \"19 \\nLinear dependence: removing column pi.1.1.\" #> [7] \"20 \\nLinear dependence: removing column pi.1.1.\" #> [8] \"21 \\nLinear dependence: removing column pi.1.1.\" #> [9] \"22 \\nLinear dependence: removing column pi.1.1.\" #> [10] \"29 \\nLinear dependence: removing column pi.1.1.\" #> [11] \"44 \\nLinear dependence: removing column pi.1.1.\" #> [1] \"14 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [2] \"15 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [3] \"16 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [4] \"17 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [5] \"18 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [6] \"19 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [7] \"20 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [8] \"21 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [9] \"22 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [1] \"14 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [2] \"15 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [3] \"17 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [4] \"18 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [5] \"20 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [6] \"21 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [1] \"14 \\nLinear dependence: removing column Repeat.GP.1.1.\" #> [2] \"17 \\nLinear dependence: removing column Repeat.GP.1.1.\" #> [3] \"20 \\nLinear dependence: removing column Repeat.GP.1.1.\" evppi(bcea_vacc, c(\"beta.1.\", \"beta.2.\"), inp$mat) #> $evppi #> [1] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [11] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [16] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [21] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [26] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [31] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.457218e-05 #> [36] 9.285676e-05 1.611413e-04 2.294259e-04 2.977105e-04 3.659951e-04 #> [41] 4.342796e-04 5.025642e-04 5.708488e-04 6.391333e-04 7.074179e-04 #> [46] 8.359579e-04 9.696656e-04 1.103373e-03 1.237081e-03 1.370789e-03 #> [51] 1.504497e-03 1.670549e-03 1.859133e-03 2.047718e-03 2.236302e-03 #> [56] 2.424887e-03 2.613472e-03 2.802056e-03 2.990641e-03 3.179226e-03 #> [61] 3.388697e-03 3.632914e-03 3.877131e-03 4.139327e-03 4.557299e-03 #> [66] 5.018772e-03 5.518913e-03 6.019054e-03 6.519195e-03 7.072839e-03 #> [71] 7.631472e-03 8.238981e-03 8.914107e-03 9.667252e-03 1.050133e-02 #> [76] 1.136910e-02 1.233047e-02 1.330277e-02 1.435958e-02 1.545712e-02 #> [81] 1.665844e-02 1.791987e-02 1.926646e-02 2.069755e-02 2.215719e-02 #> [86] 2.365858e-02 2.536231e-02 2.721777e-02 2.926248e-02 3.142167e-02 #> [91] 3.366613e-02 3.594727e-02 3.828947e-02 4.066173e-02 4.308486e-02 #> [96] 4.554495e-02 4.810142e-02 5.076759e-02 5.354944e-02 5.645627e-02 #> [101] 5.953500e-02 6.272953e-02 6.605710e-02 6.946561e-02 7.299518e-02 #> [106] 7.661519e-02 8.044882e-02 8.454452e-02 8.880028e-02 9.316356e-02 #> [111] 9.757211e-02 1.020631e-01 1.066899e-01 1.115583e-01 1.165761e-01 #> [116] 1.217236e-01 1.269848e-01 1.324089e-01 1.379159e-01 1.435288e-01 #> [121] 1.492557e-01 1.550713e-01 1.610079e-01 1.670532e-01 1.733199e-01 #> [126] 1.797311e-01 1.862892e-01 1.929569e-01 1.998673e-01 2.069650e-01 #> [131] 2.141277e-01 2.214818e-01 2.290093e-01 2.366830e-01 2.444225e-01 #> [136] 2.522186e-01 2.601369e-01 2.681379e-01 2.761958e-01 2.844056e-01 #> [141] 2.927010e-01 3.011088e-01 3.096150e-01 3.182103e-01 3.270021e-01 #> [146] 3.359932e-01 3.451267e-01 3.543843e-01 3.637561e-01 3.733013e-01 #> [151] 3.829607e-01 3.927341e-01 4.026574e-01 4.127028e-01 4.228980e-01 #> [156] 4.332371e-01 4.437379e-01 4.544137e-01 4.651813e-01 4.761334e-01 #> [161] 4.872191e-01 4.984766e-01 5.098566e-01 5.214073e-01 5.330834e-01 #> [166] 5.449266e-01 5.569304e-01 5.690002e-01 5.812412e-01 5.936338e-01 #> [171] 6.061169e-01 6.187243e-01 6.314361e-01 6.442246e-01 6.570961e-01 #> [176] 6.700288e-01 6.830269e-01 6.960982e-01 7.092648e-01 7.225512e-01 #> [181] 7.358751e-01 7.492499e-01 7.627448e-01 7.763942e-01 7.901216e-01 #> [186] 8.039977e-01 8.179865e-01 8.320791e-01 8.462536e-01 8.605366e-01 #> [191] 8.749299e-01 8.893719e-01 9.039347e-01 9.186171e-01 9.334328e-01 #> [196] 9.483216e-01 9.632936e-01 9.783535e-01 9.935304e-01 1.008831e+00 #> [201] 1.024234e+00 1.039745e+00 1.055422e+00 1.071224e+00 1.084066e+00 #> [206] 1.074809e+00 1.065660e+00 1.056641e+00 1.047729e+00 1.038873e+00 #> [211] 1.030071e+00 1.021314e+00 1.012653e+00 1.004120e+00 9.956920e-01 #> [216] 9.872935e-01 9.789248e-01 9.706356e-01 9.623851e-01 9.542037e-01 #> [221] 9.461404e-01 9.381714e-01 9.302605e-01 9.224375e-01 9.147457e-01 #> [226] 9.071125e-01 8.995306e-01 8.919891e-01 8.844713e-01 8.770460e-01 #> [231] 8.697233e-01 8.625451e-01 8.554401e-01 8.483837e-01 8.413724e-01 #> [236] 8.344054e-01 8.275071e-01 8.206365e-01 8.138200e-01 8.071060e-01 #> [241] 8.004723e-01 7.938925e-01 7.873783e-01 7.809368e-01 7.745497e-01 #> [246] 7.682354e-01 7.619523e-01 7.557112e-01 7.495586e-01 7.434515e-01 #> [251] 7.374292e-01 7.314887e-01 7.256113e-01 7.197572e-01 7.139554e-01 #> [256] 7.081910e-01 7.024605e-01 6.967964e-01 6.912702e-01 6.858125e-01 #> [261] 6.804006e-01 6.750311e-01 6.697037e-01 6.644020e-01 6.591274e-01 #> [266] 6.539432e-01 6.488189e-01 6.437494e-01 6.387192e-01 6.337315e-01 #> [271] 6.287676e-01 6.238712e-01 6.190358e-01 6.143003e-01 6.096329e-01 #> [276] 6.050127e-01 6.004741e-01 5.960047e-01 5.915586e-01 5.871403e-01 #> [281] 5.827532e-01 5.783884e-01 5.740675e-01 5.697920e-01 5.655458e-01 #> [286] 5.613352e-01 5.571441e-01 5.529855e-01 5.488534e-01 5.447722e-01 #> [291] 5.406968e-01 5.366213e-01 5.325726e-01 5.285423e-01 5.245240e-01 #> [296] 5.205150e-01 5.165845e-01 5.126795e-01 5.088076e-01 5.049948e-01 #> [301] 5.012110e-01 4.975225e-01 4.938624e-01 4.902186e-01 4.865747e-01 #> [306] 4.829573e-01 4.794062e-01 4.758743e-01 4.723592e-01 4.688739e-01 #> [311] 4.654132e-01 4.619793e-01 4.585647e-01 4.551584e-01 4.517521e-01 #> [316] 4.483486e-01 4.449656e-01 4.416163e-01 4.383141e-01 4.350518e-01 #> [321] 4.318246e-01 4.286196e-01 4.254631e-01 4.223070e-01 4.191951e-01 #> [326] 4.161302e-01 4.131096e-01 4.101110e-01 4.071347e-01 4.041827e-01 #> [331] 4.012589e-01 3.983743e-01 3.955212e-01 3.927254e-01 3.899432e-01 #> [336] 3.871855e-01 3.844689e-01 3.817796e-01 3.791394e-01 3.765335e-01 #> [341] 3.739600e-01 3.714164e-01 3.688934e-01 3.663704e-01 3.638535e-01 #> [346] 3.613474e-01 3.588413e-01 3.563576e-01 3.539089e-01 3.514843e-01 #> [351] 3.490616e-01 3.466548e-01 3.442652e-01 3.418779e-01 3.395314e-01 #> [356] 3.372204e-01 3.349244e-01 3.326430e-01 3.303822e-01 3.281223e-01 #> [361] 3.258784e-01 3.236347e-01 3.214071e-01 3.191795e-01 3.169807e-01 #> [366] 3.148009e-01 3.126353e-01 3.105210e-01 3.084279e-01 3.063603e-01 #> [371] 3.043336e-01 3.023203e-01 3.003220e-01 2.983323e-01 2.963581e-01 #> [376] 2.944046e-01 2.924985e-01 2.906183e-01 2.887512e-01 2.869099e-01 #> [381] 2.850765e-01 2.832430e-01 2.814097e-01 2.796030e-01 2.778181e-01 #> [386] 2.760596e-01 2.743042e-01 2.725626e-01 2.708210e-01 2.690877e-01 #> [391] 2.673614e-01 2.656351e-01 2.639213e-01 2.622214e-01 2.605248e-01 #> [396] 2.588283e-01 2.571434e-01 2.554615e-01 2.537810e-01 2.521252e-01 #> [401] 2.504935e-01 2.488892e-01 2.472959e-01 2.457275e-01 2.441923e-01 #> [406] 2.426613e-01 2.411526e-01 2.396684e-01 2.382291e-01 2.367951e-01 #> [411] 2.353736e-01 2.339666e-01 2.325611e-01 2.311557e-01 2.297563e-01 #> [416] 2.283653e-01 2.269863e-01 2.256259e-01 2.242938e-01 2.229744e-01 #> [421] 2.216551e-01 2.203358e-01 2.190165e-01 2.176971e-01 2.163804e-01 #> [426] 2.150968e-01 2.138344e-01 2.125824e-01 2.113339e-01 2.100859e-01 #> [431] 2.088515e-01 2.076339e-01 2.064492e-01 2.052776e-01 2.041179e-01 #> [436] 2.029666e-01 2.018338e-01 2.007256e-01 1.996347e-01 1.985587e-01 #> [441] 1.974892e-01 1.964197e-01 1.953502e-01 1.942885e-01 1.932456e-01 #> [446] 1.922157e-01 1.911862e-01 1.901613e-01 1.891454e-01 1.881421e-01 #> [451] 1.871387e-01 1.861354e-01 1.851410e-01 1.841523e-01 1.831884e-01 #> [456] 1.822613e-01 1.813469e-01 1.804353e-01 1.795236e-01 1.786235e-01 #> [461] 1.777250e-01 1.768265e-01 1.759280e-01 1.750408e-01 1.741682e-01 #> [466] 1.733006e-01 1.724417e-01 1.715828e-01 1.707240e-01 1.698655e-01 #> [471] 1.690192e-01 1.681822e-01 1.673487e-01 1.665152e-01 1.656817e-01 #> [476] 1.648482e-01 1.640322e-01 1.632500e-01 1.624679e-01 1.616857e-01 #> [481] 1.609060e-01 1.601366e-01 1.593672e-01 1.585978e-01 1.578284e-01 #> [486] 1.570590e-01 1.562896e-01 1.555302e-01 1.547736e-01 1.540170e-01 #> [491] 1.532604e-01 1.525038e-01 1.517472e-01 1.509906e-01 1.502340e-01 #> [496] 1.494882e-01 1.487427e-01 1.479973e-01 1.472519e-01 1.465064e-01 #> [501] 1.457650e-01 #> #> $index #> [1] \"beta.1.\" \"beta.2.\" #> #> $k #> [1] 0 100 200 300 400 500 600 700 800 900 1000 1100 #> [13] 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 #> [25] 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 #> [37] 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 #> [49] 4800 4900 5000 5100 5200 5300 5400 5500 5600 5700 5800 5900 #> [61] 6000 6100 6200 6300 6400 6500 6600 6700 6800 6900 7000 7100 #> [73] 7200 7300 7400 7500 7600 7700 7800 7900 8000 8100 8200 8300 #> [85] 8400 8500 8600 8700 8800 8900 9000 9100 9200 9300 9400 9500 #> [97] 9600 9700 9800 9900 10000 10100 10200 10300 10400 10500 10600 10700 #> [109] 10800 10900 11000 11100 11200 11300 11400 11500 11600 11700 11800 11900 #> [121] 12000 12100 12200 12300 12400 12500 12600 12700 12800 12900 13000 13100 #> [133] 13200 13300 13400 13500 13600 13700 13800 13900 14000 14100 14200 14300 #> [145] 14400 14500 14600 14700 14800 14900 15000 15100 15200 15300 15400 15500 #> [157] 15600 15700 15800 15900 16000 16100 16200 16300 16400 16500 16600 16700 #> [169] 16800 16900 17000 17100 17200 17300 17400 17500 17600 17700 17800 17900 #> [181] 18000 18100 18200 18300 18400 18500 18600 18700 18800 18900 19000 19100 #> [193] 19200 19300 19400 19500 19600 19700 19800 19900 20000 20100 20200 20300 #> [205] 20400 20500 20600 20700 20800 20900 21000 21100 21200 21300 21400 21500 #> [217] 21600 21700 21800 21900 22000 22100 22200 22300 22400 22500 22600 22700 #> [229] 22800 22900 23000 23100 23200 23300 23400 23500 23600 23700 23800 23900 #> [241] 24000 24100 24200 24300 24400 24500 24600 24700 24800 24900 25000 25100 #> [253] 25200 25300 25400 25500 25600 25700 25800 25900 26000 26100 26200 26300 #> [265] 26400 26500 26600 26700 26800 26900 27000 27100 27200 27300 27400 27500 #> [277] 27600 27700 27800 27900 28000 28100 28200 28300 28400 28500 28600 28700 #> [289] 28800 28900 29000 29100 29200 29300 29400 29500 29600 29700 29800 29900 #> [301] 30000 30100 30200 30300 30400 30500 30600 30700 30800 30900 31000 31100 #> [313] 31200 31300 31400 31500 31600 31700 31800 31900 32000 32100 32200 32300 #> [325] 32400 32500 32600 32700 32800 32900 33000 33100 33200 33300 33400 33500 #> [337] 33600 33700 33800 33900 34000 34100 34200 34300 34400 34500 34600 34700 #> [349] 34800 34900 35000 35100 35200 35300 35400 35500 35600 35700 35800 35900 #> [361] 36000 36100 36200 36300 36400 36500 36600 36700 36800 36900 37000 37100 #> [373] 37200 37300 37400 37500 37600 37700 37800 37900 38000 38100 38200 38300 #> [385] 38400 38500 38600 38700 38800 38900 39000 39100 39200 39300 39400 39500 #> [397] 39600 39700 39800 39900 40000 40100 40200 40300 40400 40500 40600 40700 #> [409] 40800 40900 41000 41100 41200 41300 41400 41500 41600 41700 41800 41900 #> [421] 42000 42100 42200 42300 42400 42500 42600 42700 42800 42900 43000 43100 #> [433] 43200 43300 43400 43500 43600 43700 43800 43900 44000 44100 44200 44300 #> [445] 44400 44500 44600 44700 44800 44900 45000 45100 45200 45300 45400 45500 #> [457] 45600 45700 45800 45900 46000 46100 46200 46300 46400 46500 46600 46700 #> [469] 46800 46900 47000 47100 47200 47300 47400 47500 47600 47700 47800 47900 #> [481] 48000 48100 48200 48300 48400 48500 48600 48700 48800 48900 49000 49100 #> [493] 49200 49300 49400 49500 49600 49700 49800 49900 50000 #> #> $evi #> [1] 0.03705361 0.03785587 0.03869327 0.03957261 0.04053147 0.04149032 #> [7] 0.04249547 0.04357790 0.04468983 0.04580177 0.04696679 0.04820919 #> [13] 0.04945159 0.05071720 0.05204083 0.05341006 0.05479360 0.05628177 #> [19] 0.05790349 0.05968408 0.06163395 0.06371142 0.06579055 0.06796376 #> [25] 0.07022097 0.07273816 0.07532847 0.07795127 0.08057406 0.08320268 #> [31] 0.08589638 0.08868468 0.09152601 0.09437325 0.09732932 0.10029796 #> [37] 0.10336608 0.10650386 0.10969266 0.11291187 0.11623517 0.11961608 #> [43] 0.12301796 0.12655367 0.13016897 0.13389347 0.13771150 0.14164319 #> [49] 0.14562582 0.14972633 0.15408068 0.15855398 0.16308971 0.16773032 #> [55] 0.17246620 0.17733774 0.18240585 0.18763480 0.19302758 0.19870097 #> [61] 0.20455777 0.21058793 0.21671040 0.22296970 0.22947367 0.23630862 #> [67] 0.24330447 0.25039355 0.25766800 0.26518678 0.27299674 0.28116086 #> [73] 0.28956168 0.29817600 0.30710155 0.31640030 0.32590075 0.33562984 #> [79] 0.34549466 0.35556390 0.36586862 0.37636890 0.38722042 0.39819986 #> [85] 0.40931334 0.42067277 0.43210667 0.44377365 0.45566760 0.46785113 #> [91] 0.48014364 0.49246093 0.50499804 0.51762457 0.53037623 0.54325794 #> [97] 0.55623809 0.56931969 0.58247875 0.59587792 0.60962413 0.62343508 #> [103] 0.63739430 0.65146528 0.66560388 0.67977458 0.69403487 0.70842482 #> [109] 0.72293925 0.73755255 0.75234332 0.76723566 0.78221180 0.79721725 #> [115] 0.81232276 0.82758674 0.84296480 0.85844709 0.87398274 0.88966269 #> [121] 0.90550552 0.92141819 0.93740388 0.95345109 0.96960001 0.98586393 #> [127] 1.00217636 1.01852827 1.03494208 1.05142297 1.06797820 1.08468101 #> [133] 1.10149448 1.11850610 1.13565841 1.15294406 1.17038384 1.18787278 #> [139] 1.20544370 1.22316318 1.24098583 1.25890098 1.27685790 1.29494468 #> [145] 1.31305675 1.33121282 1.34951452 1.36794968 1.38653595 1.40518347 #> [151] 1.42398997 1.44291220 1.46187667 1.48090091 1.49998386 1.51913333 #> [157] 1.53842038 1.55785893 1.57742357 1.59708499 1.61685411 1.63671449 #> [163] 1.65661087 1.67656286 1.69652816 1.71649345 1.73649008 1.75651452 #> [169] 1.77658853 1.79668980 1.81684907 1.83704353 1.85732385 1.87768519 #> [175] 1.89813142 1.91868758 1.93931951 1.95998689 1.98070096 2.00151878 #> [181] 2.02248963 2.04353027 2.06465898 2.08582184 2.10699995 2.12828009 #> [187] 2.14966652 2.17114009 2.19267665 2.21423926 2.23585252 2.25753717 #> [193] 2.27924027 2.30098203 2.32278791 2.34466165 2.36659499 2.38854408 #> [199] 2.41055686 2.43261442 2.45475044 2.47693925 2.49917875 2.52143631 #> [205] 2.54070684 2.53787999 2.53506303 2.53229036 2.52958762 2.52697130 #> [211] 2.52440752 2.52185173 2.51932424 2.51682773 2.51436495 2.51199912 #> [217] 2.50972006 2.50748163 2.50529882 2.50313412 2.50097250 2.49883204 #> [223] 2.49676430 2.49474058 2.49271685 2.49069313 2.48867961 2.48671075 #> [229] 2.48488026 2.48307698 2.48130197 2.47958992 2.47791258 2.47625725 #> [235] 2.47464086 2.47306603 2.47152238 2.46999548 2.46856835 2.46717635 #> [241] 2.46580518 2.46446047 2.46314796 2.46185416 2.46056499 2.45930632 #> [247] 2.45807492 2.45694841 2.45588064 2.45485168 2.45384951 2.45290828 #> [253] 2.45201256 2.45115357 2.45034345 2.44954816 2.44877788 2.44801321 #> [259] 2.44724855 2.44648588 2.44575366 2.44502992 2.44434475 2.44368189 #> [265] 2.44304942 2.44246395 2.44190999 2.44138249 2.44092506 2.44048420 #> [271] 2.44005376 2.43966976 2.43930692 2.43896298 2.43867630 2.43841126 #> [277] 2.43815833 2.43792271 2.43770326 2.43750768 2.43734001 2.43717648 #> [283] 2.43701295 2.43685240 2.43672731 2.43660228 2.43649001 2.43639935 #> [289] 2.43632504 2.43627307 2.43624400 2.43625683 2.43628988 2.43635282 #> [295] 2.43644101 2.43653657 2.43664089 2.43676890 2.43691328 2.43709723 #> [301] 2.43728708 2.43747693 2.43766678 2.43785663 2.43804648 2.43823633 #> [307] 2.43844428 2.43869049 2.43895177 2.43921999 2.43948822 2.43975645 #> [313] 2.44002468 2.44029291 2.44056114 2.44082937 2.44109760 2.44137953 #> [319] 2.44170391 2.44205164 2.44241982 2.44282017 2.44323328 2.44364639 #> [325] 2.44409249 2.44457061 2.44507864 2.44562174 2.44616484 2.44670794 #> [331] 2.44725127 2.44781211 2.44838171 2.44895677 2.44954877 2.45015945 #> [337] 2.45077012 2.45139695 2.45203779 2.45268564 2.45333350 2.45398136 #> [343] 2.45462922 2.45528430 2.45595980 2.45663861 2.45733166 2.45803623 #> [349] 2.45876134 2.45951256 2.46027210 2.46104954 2.46184788 2.46270278 #> [355] 2.46356630 2.46442982 2.46529334 2.46616454 2.46705682 2.46794910 #> [361] 2.46884707 2.46975739 2.47066771 2.47157803 2.47250829 2.47345963 #> [367] 2.47441097 2.47536231 2.47631365 2.47726499 2.47821633 2.47916766 #> [373] 2.48011900 2.48108604 2.48205474 2.48302345 2.48399401 2.48498194 #> [379] 2.48596986 2.48696203 2.48797773 2.48900137 2.49002501 2.49106195 #> [385] 2.49210191 2.49314802 2.49419569 2.49526209 2.49633230 2.49740252 #> [391] 2.49847482 2.49956060 2.50065377 2.50174694 2.50284011 2.50393327 #> [397] 2.50502644 2.50613166 2.50724137 2.50835109 2.50946173 2.51058409 #> [403] 2.51171324 2.51285383 2.51399442 2.51515263 2.51631514 2.51748307 #> [409] 2.51865224 2.51982141 2.52100290 2.52218565 2.52336840 2.52455115 #> [415] 2.52573390 2.52692109 2.52811219 2.52933476 2.53058895 2.53185552 #> [421] 2.53312209 2.53441819 2.53575671 2.53709574 2.53846228 2.53987875 #> [427] 2.54131648 2.54276301 2.54421702 2.54567692 2.54713737 2.54861863 #> [433] 2.55010724 2.55161064 2.55311404 2.55461744 2.55612084 2.55762424 #> [439] 2.55912764 2.56063104 2.56213444 2.56364595 2.56516720 2.56668845 #> [445] 2.56821847 2.56975283 2.57129950 2.57284964 2.57442273 2.57600088 #> [451] 2.57757903 2.57916437 2.58076660 2.58238097 2.58400753 2.58565356 #> [457] 2.58730489 2.58896026 2.59062565 2.59229225 2.59396413 2.59565871 #> [463] 2.59736472 2.59909129 2.60083599 2.60258363 2.60433128 2.60607892 #> [469] 2.60782656 2.60957421 2.61132185 2.61306949 2.61482472 2.61658627 #> [475] 2.61835210 2.62011955 2.62188699 2.62365443 2.62542187 2.62718931 #> [481] 2.62896830 2.63074890 2.63253189 2.63431941 2.63611797 2.63793732 #> [487] 2.63975777 2.64157823 2.64339903 2.64522609 2.64705546 2.64888724 #> [493] 2.65071902 2.65255080 2.65440805 2.65627824 2.65815205 2.66002586 #> [499] 2.66189967 2.66377348 2.66564729 #> #> $parameters #> [1] \"beta.1. and beta.2.\" #> #> $time #> $time$`Fitting for Effects` #> NULL #> #> $time$`Fitting for Costs` #> NULL #> #> $time$`Calculating EVPPI` #> NULL #> #> #> $method #> $method$`Methods for Effects` #> [1] \"gam\" #> #> $method$`Methods for Costs` #> [1] \"gam\" #> #> #> $fitted.costs #> ...1 #> [1,] 5.539060 0 #> [2,] 5.042096 0 #> [3,] 5.420907 0 #> [4,] 5.738414 0 #> [5,] 5.469780 0 #> [6,] 5.552250 0 #> [7,] 3.622860 0 #> [8,] 6.049415 0 #> [9,] 5.829584 0 #> [10,] 4.978065 0 #> [11,] 4.429175 0 #> [12,] 5.407992 0 #> [13,] 6.090357 0 #> [14,] 6.110306 0 #> [15,] 4.868019 0 #> [16,] 5.264807 0 #> [17,] 5.938002 0 #> [18,] 4.435298 0 #> [19,] 4.992994 0 #> [20,] 5.138636 0 #> [21,] 5.692185 0 #> [22,] 5.451911 0 #> [23,] 5.696216 0 #> [24,] 5.172949 0 #> [25,] 4.411596 0 #> [26,] 4.540321 0 #> [27,] 6.219929 0 #> [28,] 5.880996 0 #> [29,] 5.089494 0 #> [30,] 5.046960 0 #> [31,] 5.684144 0 #> [32,] 3.806804 0 #> [33,] 5.209105 0 #> [34,] 5.301564 0 #> [35,] 5.106181 0 #> [36,] 5.813065 0 #> [37,] 4.163367 0 #> [38,] 5.291885 0 #> [39,] 5.057783 0 #> [40,] 5.640054 0 #> [41,] 5.212866 0 #> [42,] 5.686964 0 #> [43,] 5.509276 0 #> [44,] 4.560746 0 #> [45,] 5.257646 0 #> [46,] 5.586814 0 #> [47,] 5.714918 0 #> [48,] 5.374325 0 #> [49,] 5.181640 0 #> [50,] 6.092522 0 #> [51,] 5.040795 0 #> [52,] 3.921548 0 #> [53,] 6.035031 0 #> [54,] 5.881642 0 #> [55,] 5.361438 0 #> [56,] 6.353782 0 #> [57,] 5.481254 0 #> [58,] 5.536473 0 #> [59,] 5.249361 0 #> [60,] 5.351821 0 #> [61,] 4.919147 0 #> [62,] 5.741087 0 #> [63,] 4.555483 0 #> [64,] 5.829663 0 #> [65,] 4.456022 0 #> [66,] 4.756325 0 #> [67,] 5.156087 0 #> [68,] 4.299859 0 #> [69,] 4.859611 0 #> [70,] 4.520524 0 #> [71,] 4.270351 0 #> [72,] 5.854351 0 #> [73,] 4.204380 0 #> [74,] 5.162071 0 #> [75,] 5.889816 0 #> [76,] 4.742549 0 #> [77,] 5.483039 0 #> [78,] 4.585330 0 #> [79,] 5.076819 0 #> [80,] 4.929809 0 #> [81,] 5.851112 0 #> [82,] 6.150576 0 #> [83,] 5.039752 0 #> [84,] 4.184469 0 #> [85,] 5.557155 0 #> [86,] 3.080087 0 #> [87,] 5.447201 0 #> [88,] 5.299291 0 #> [89,] 4.586296 0 #> [90,] 5.248812 0 #> [91,] 3.693466 0 #> [92,] 4.814863 0 #> [93,] 5.685345 0 #> [94,] 4.713743 0 #> [95,] 4.499628 0 #> [96,] 3.619984 0 #> [97,] 4.879671 0 #> [98,] 4.647593 0 #> [99,] 5.920827 0 #> [100,] 6.020268 0 #> [101,] 4.651883 0 #> [102,] 5.377532 0 #> [103,] 5.025746 0 #> [104,] 4.793158 0 #> [105,] 3.982976 0 #> [106,] 4.550494 0 #> [107,] 5.395733 0 #> [108,] 5.616294 0 #> [109,] 6.198823 0 #> [110,] 6.239986 0 #> [111,] 4.160499 0 #> [112,] 5.684216 0 #> [113,] 6.312563 0 #> [114,] 4.996849 0 #> [115,] 4.215937 0 #> [116,] 4.857364 0 #> [117,] 4.417169 0 #> [118,] 3.308411 0 #> [119,] 4.758624 0 #> [120,] 5.334703 0 #> [121,] 5.117974 0 #> [122,] 5.452513 0 #> [123,] 5.616120 0 #> [124,] 5.031824 0 #> [125,] 5.906992 0 #> [126,] 5.201315 0 #> [127,] 5.199154 0 #> [128,] 6.321913 0 #> [129,] 5.327667 0 #> [130,] 4.656508 0 #> [131,] 5.418207 0 #> [132,] 5.610628 0 #> [133,] 4.035627 0 #> [134,] 4.090451 0 #> [135,] 5.323000 0 #> [136,] 5.161108 0 #> [137,] 5.628860 0 #> [138,] 6.002209 0 #> [139,] 5.940414 0 #> [140,] 5.951488 0 #> [141,] 4.155158 0 #> [142,] 4.305728 0 #> [143,] 5.811871 0 #> [144,] 3.968335 0 #> [145,] 4.683313 0 #> [146,] 4.813944 0 #> [147,] 5.717913 0 #> [148,] 3.841804 0 #> [149,] 5.448253 0 #> [150,] 6.119106 0 #> [151,] 5.366319 0 #> [152,] 5.886763 0 #> [153,] 6.044123 0 #> [154,] 5.484369 0 #> [155,] 5.156000 0 #> [156,] 6.495019 0 #> [157,] 5.582958 0 #> [158,] 4.897245 0 #> [159,] 5.068719 0 #> [160,] 4.308136 0 #> [161,] 4.848514 0 #> [162,] 4.541059 0 #> [163,] 5.500579 0 #> [164,] 5.744604 0 #> [165,] 4.803044 0 #> [166,] 4.975764 0 #> [167,] 5.303851 0 #> [168,] 5.046976 0 #> [169,] 5.036179 0 #> [170,] 4.239431 0 #> [171,] 6.274060 0 #> [172,] 4.101257 0 #> [173,] 6.202505 0 #> [174,] 4.269867 0 #> [175,] 5.798713 0 #> [176,] 4.612728 0 #> [177,] 5.447640 0 #> [178,] 5.744765 0 #> [179,] 4.769053 0 #> [180,] 5.942330 0 #> [181,] 4.969693 0 #> [182,] 5.259084 0 #> [183,] 5.637955 0 #> [184,] 5.069238 0 #> [185,] 6.448760 0 #> [186,] 5.282090 0 #> [187,] 4.093503 0 #> [188,] 4.514099 0 #> [189,] 6.195264 0 #> [190,] 5.580690 0 #> [191,] 5.291420 0 #> [192,] 5.241205 0 #> [193,] 4.601338 0 #> [194,] 4.753936 0 #> [195,] 4.537685 0 #> [196,] 3.874407 0 #> [197,] 6.430400 0 #> [198,] 5.854666 0 #> [199,] 3.407401 0 #> [200,] 4.170614 0 #> [201,] 5.680480 0 #> [202,] 5.705507 0 #> [203,] 4.802852 0 #> [204,] 5.122291 0 #> [205,] 5.146346 0 #> [206,] 4.268045 0 #> [207,] 5.107833 0 #> [208,] 6.245946 0 #> [209,] 4.418758 0 #> [210,] 4.432187 0 #> [211,] 4.216319 0 #> [212,] 5.158100 0 #> [213,] 5.057736 0 #> [214,] 4.340614 0 #> [215,] 5.221258 0 #> [216,] 4.560710 0 #> [217,] 5.550095 0 #> [218,] 6.486105 0 #> [219,] 5.039445 0 #> [220,] 5.324935 0 #> [221,] 5.279964 0 #> [222,] 5.281816 0 #> [223,] 5.442170 0 #> [224,] 6.229507 0 #> [225,] 6.021018 0 #> [226,] 5.391458 0 #> [227,] 5.515263 0 #> [228,] 4.397692 0 #> [229,] 5.562380 0 #> [230,] 5.478475 0 #> [231,] 4.886453 0 #> [232,] 5.725884 0 #> [233,] 5.616004 0 #> [234,] 5.918790 0 #> [235,] 5.738133 0 #> [236,] 5.455824 0 #> [237,] 5.012434 0 #> [238,] 4.565855 0 #> [239,] 4.845516 0 #> [240,] 4.021740 0 #> [241,] 4.278009 0 #> [242,] 5.138652 0 #> [243,] 5.719739 0 #> [244,] 5.738399 0 #> [245,] 5.538478 0 #> [246,] 6.272382 0 #> [247,] 5.937437 0 #> [248,] 4.974556 0 #> [249,] 5.439875 0 #> [250,] 4.486892 0 #> [251,] 6.008683 0 #> [252,] 5.907629 0 #> [253,] 5.868258 0 #> [254,] 4.686085 0 #> [255,] 4.894189 0 #> [256,] 4.890957 0 #> [257,] 4.778962 0 #> [258,] 5.495533 0 #> [259,] 5.281087 0 #> [260,] 5.177108 0 #> [261,] 5.755615 0 #> [262,] 4.332822 0 #> [263,] 5.036257 0 #> [264,] 5.156907 0 #> [265,] 5.848136 0 #> [266,] 5.232010 0 #> [267,] 5.015574 0 #> [268,] 5.783073 0 #> [269,] 4.887681 0 #> [270,] 3.879547 0 #> [271,] 6.770099 0 #> [272,] 5.724358 0 #> [273,] 5.056678 0 #> [274,] 6.084100 0 #> [275,] 5.550148 0 #> [276,] 4.605858 0 #> [277,] 4.533346 0 #> [278,] 6.586399 0 #> [279,] 5.475804 0 #> [280,] 3.999786 0 #> [281,] 4.821916 0 #> [282,] 5.428940 0 #> [283,] 4.965619 0 #> [284,] 5.156365 0 #> [285,] 5.471457 0 #> [286,] 4.532253 0 #> [287,] 5.128821 0 #> [288,] 4.376665 0 #> [289,] 4.789164 0 #> [290,] 5.531183 0 #> [291,] 5.021224 0 #> [292,] 3.982473 0 #> [293,] 5.900801 0 #> [294,] 5.613028 0 #> [295,] 4.501413 0 #> [296,] 5.369793 0 #> [297,] 5.445729 0 #> [298,] 6.061728 0 #> [299,] 5.816942 0 #> [300,] 4.346642 0 #> [301,] 4.780172 0 #> [302,] 5.473171 0 #> [303,] 6.469736 0 #> [304,] 5.021547 0 #> [305,] 4.293773 0 #> [306,] 4.854917 0 #> [307,] 4.440534 0 #> [308,] 5.045389 0 #> [309,] 6.169920 0 #> [310,] 6.011702 0 #> [311,] 5.694597 0 #> [312,] 6.806970 0 #> [313,] 5.171469 0 #> [314,] 4.848553 0 #> [315,] 4.822993 0 #> [316,] 4.723618 0 #> [317,] 4.188315 0 #> [318,] 5.085701 0 #> [319,] 5.133842 0 #> [320,] 5.504449 0 #> [321,] 5.652670 0 #> [322,] 4.303276 0 #> [323,] 4.760278 0 #> [324,] 5.363784 0 #> [325,] 5.513805 0 #> [326,] 5.720236 0 #> [327,] 5.606547 0 #> [328,] 5.042369 0 #> [329,] 5.249603 0 #> [330,] 5.670955 0 #> [331,] 5.067745 0 #> [332,] 5.798864 0 #> [333,] 4.193601 0 #> [334,] 5.219055 0 #> [335,] 4.774512 0 #> [336,] 6.295426 0 #> [337,] 5.539983 0 #> [338,] 5.925282 0 #> [339,] 4.844867 0 #> [340,] 5.329984 0 #> [341,] 4.360776 0 #> [342,] 5.011469 0 #> [343,] 5.246450 0 #> [344,] 6.041730 0 #> [345,] 5.877811 0 #> [346,] 4.485290 0 #> [347,] 4.669637 0 #> [348,] 5.287495 0 #> [349,] 5.415794 0 #> [350,] 5.186906 0 #> [351,] 4.341641 0 #> [352,] 5.507438 0 #> [353,] 4.759675 0 #> [354,] 4.790569 0 #> [355,] 5.565071 0 #> [356,] 5.529241 0 #> [357,] 5.019743 0 #> [358,] 3.817816 0 #> [359,] 4.136043 0 #> [360,] 4.792871 0 #> [361,] 4.785833 0 #> [362,] 4.566678 0 #> [363,] 4.619853 0 #> [364,] 5.273044 0 #> [365,] 6.009045 0 #> [366,] 6.257479 0 #> [367,] 4.815202 0 #> [368,] 5.350234 0 #> [369,] 5.271335 0 #> [370,] 5.173084 0 #> [371,] 4.853887 0 #> [372,] 5.069487 0 #> [373,] 5.449406 0 #> [374,] 4.084644 0 #> [375,] 4.819726 0 #> [376,] 5.752519 0 #> [377,] 4.884872 0 #> [378,] 5.266579 0 #> [379,] 4.680861 0 #> [380,] 4.059640 0 #> [381,] 5.693785 0 #> [382,] 5.764346 0 #> [383,] 4.690388 0 #> [384,] 5.433501 0 #> [385,] 5.338777 0 #> [386,] 3.719235 0 #> [387,] 4.696649 0 #> [388,] 4.045716 0 #> [389,] 5.553352 0 #> [390,] 4.880399 0 #> [391,] 6.413306 0 #> [392,] 5.858073 0 #> [393,] 5.817244 0 #> [394,] 4.459265 0 #> [395,] 5.412586 0 #> [396,] 4.828992 0 #> [397,] 5.420323 0 #> [398,] 5.412676 0 #> [399,] 5.166367 0 #> [400,] 6.068486 0 #> [401,] 4.498203 0 #> [402,] 4.954914 0 #> [403,] 4.772686 0 #> [404,] 6.227671 0 #> [405,] 4.059127 0 #> [406,] 5.607136 0 #> [407,] 4.914351 0 #> [408,] 5.475194 0 #> [409,] 5.700530 0 #> [410,] 5.752671 0 #> [411,] 5.496542 0 #> [412,] 4.446989 0 #> [413,] 6.359405 0 #> [414,] 5.397362 0 #> [415,] 6.078466 0 #> [416,] 4.500891 0 #> [417,] 5.621780 0 #> [418,] 5.895566 0 #> [419,] 5.339590 0 #> [420,] 4.991008 0 #> [421,] 5.983944 0 #> [422,] 4.914116 0 #> [423,] 5.382246 0 #> [424,] 5.488874 0 #> [425,] 4.263182 0 #> [426,] 5.410025 0 #> [427,] 5.832342 0 #> [428,] 5.321753 0 #> [429,] 6.225024 0 #> [430,] 4.514543 0 #> [431,] 4.872656 0 #> [432,] 5.517399 0 #> [433,] 4.238021 0 #> [434,] 4.959032 0 #> [435,] 5.232736 0 #> [436,] 4.372003 0 #> [437,] 5.104239 0 #> [438,] 4.925713 0 #> [439,] 4.349524 0 #> [440,] 5.688364 0 #> [441,] 5.272597 0 #> [442,] 5.058150 0 #> [443,] 4.905186 0 #> [444,] 5.116603 0 #> [445,] 4.976429 0 #> [446,] 5.464805 0 #> [447,] 4.937833 0 #> [448,] 4.015283 0 #> [449,] 4.834030 0 #> [450,] 4.277362 0 #> [451,] 4.639724 0 #> [452,] 4.542908 0 #> [453,] 4.881269 0 #> [454,] 6.274252 0 #> [455,] 5.999787 0 #> [456,] 4.200631 0 #> [457,] 4.811412 0 #> [458,] 5.685109 0 #> [459,] 4.970131 0 #> [460,] 5.056624 0 #> [461,] 4.144599 0 #> [462,] 5.201531 0 #> [463,] 6.442667 0 #> [464,] 6.273942 0 #> [465,] 4.569216 0 #> [466,] 5.283486 0 #> [467,] 5.640554 0 #> [468,] 4.780244 0 #> [469,] 5.129521 0 #> [470,] 4.252870 0 #> [471,] 4.624672 0 #> [472,] 4.500011 0 #> [473,] 4.555120 0 #> [474,] 4.559019 0 #> [475,] 5.434200 0 #> [476,] 5.627229 0 #> [477,] 5.138669 0 #> [478,] 3.163727 0 #> [479,] 4.238129 0 #> [480,] 4.734084 0 #> [481,] 2.883787 0 #> [482,] 5.962383 0 #> [483,] 5.561811 0 #> [484,] 5.758392 0 #> [485,] 6.405401 0 #> [486,] 5.870972 0 #> [487,] 5.229085 0 #> [488,] 5.601664 0 #> [489,] 5.680402 0 #> [490,] 2.297103 0 #> [491,] 4.143709 0 #> [492,] 4.338752 0 #> [493,] 3.885268 0 #> [494,] 6.210636 0 #> [495,] 4.441624 0 #> [496,] 5.282179 0 #> [497,] 5.187344 0 #> [498,] 6.167678 0 #> [499,] 5.003714 0 #> [500,] 5.034583 0 #> [501,] 4.319677 0 #> [502,] 5.443731 0 #> [503,] 4.824521 0 #> [504,] 5.669115 0 #> [505,] 5.930372 0 #> [506,] 4.879292 0 #> [507,] 4.081044 0 #> [508,] 5.643155 0 #> [509,] 5.015277 0 #> [510,] 4.673759 0 #> [511,] 5.199560 0 #> [512,] 4.378753 0 #> [513,] 5.141009 0 #> [514,] 5.179615 0 #> [515,] 3.712490 0 #> [516,] 5.237060 0 #> [517,] 5.775917 0 #> [518,] 4.871324 0 #> [519,] 4.071617 0 #> [520,] 5.994396 0 #> [521,] 5.189757 0 #> [522,] 5.404872 0 #> [523,] 5.978365 0 #> [524,] 5.152467 0 #> [525,] 6.095778 0 #> [526,] 5.366312 0 #> [527,] 5.675808 0 #> [528,] 5.342810 0 #> [529,] 4.098486 0 #> [530,] 6.670130 0 #> [531,] 5.307000 0 #> [532,] 5.600181 0 #> [533,] 4.965255 0 #> [534,] 4.934772 0 #> [535,] 5.439599 0 #> [536,] 5.413750 0 #> [537,] 4.780892 0 #> [538,] 4.056149 0 #> [539,] 5.355559 0 #> [540,] 6.725569 0 #> [541,] 5.231151 0 #> [542,] 5.524804 0 #> [543,] 4.430890 0 #> [544,] 4.043705 0 #> [545,] 5.681395 0 #> [546,] 4.875717 0 #> [547,] 4.712016 0 #> [548,] 4.337122 0 #> [549,] 3.621597 0 #> [550,] 5.727064 0 #> [551,] 5.948722 0 #> [552,] 5.218302 0 #> [553,] 3.837108 0 #> [554,] 6.591027 0 #> [555,] 4.306567 0 #> [556,] 4.690052 0 #> [557,] 5.326873 0 #> [558,] 4.660598 0 #> [559,] 4.873312 0 #> [560,] 5.546182 0 #> [561,] 5.305335 0 #> [562,] 5.404546 0 #> [563,] 4.438375 0 #> [564,] 5.551944 0 #> [565,] 6.086414 0 #> [566,] 5.388023 0 #> [567,] 4.527011 0 #> [568,] 5.351103 0 #> [569,] 4.998643 0 #> [570,] 5.679968 0 #> [571,] 5.460582 0 #> [572,] 5.545496 0 #> [573,] 4.872348 0 #> [574,] 5.279657 0 #> [575,] 3.759895 0 #> [576,] 4.235306 0 #> [577,] 4.517857 0 #> [578,] 5.203312 0 #> [579,] 5.154415 0 #> [580,] 5.603429 0 #> [581,] 3.908157 0 #> [582,] 4.593308 0 #> [583,] 3.010580 0 #> [584,] 5.598714 0 #> [585,] 5.515450 0 #> [586,] 4.767280 0 #> [587,] 5.180259 0 #> [588,] 5.251397 0 #> [589,] 6.523599 0 #> [590,] 6.007581 0 #> [591,] 5.248788 0 #> [592,] 5.081477 0 #> [593,] 5.032266 0 #> [594,] 3.771727 0 #> [595,] 4.863446 0 #> [596,] 3.972018 0 #> [597,] 6.249646 0 #> [598,] 4.984692 0 #> [599,] 5.988938 0 #> [600,] 4.848311 0 #> [601,] 3.900215 0 #> [602,] 4.411661 0 #> [603,] 5.970272 0 #> [604,] 5.920707 0 #> [605,] 6.017723 0 #> [606,] 6.457222 0 #> [607,] 6.229802 0 #> [608,] 4.678008 0 #> [609,] 5.079349 0 #> [610,] 4.625489 0 #> [611,] 3.929851 0 #> [612,] 4.150531 0 #> [613,] 5.537036 0 #> [614,] 5.300277 0 #> [615,] 5.209738 0 #> [616,] 5.093470 0 #> [617,] 5.092428 0 #> [618,] 5.695194 0 #> [619,] 5.436593 0 #> [620,] 5.447769 0 #> [621,] 5.196907 0 #> [622,] 4.936854 0 #> [623,] 3.701986 0 #> [624,] 4.726856 0 #> [625,] 5.824369 0 #> [626,] 5.314725 0 #> [627,] 5.610858 0 #> [628,] 6.044890 0 #> [629,] 4.974855 0 #> [630,] 5.696964 0 #> [631,] 4.609722 0 #> [632,] 5.117806 0 #> [633,] 4.709817 0 #> [634,] 4.389617 0 #> [635,] 5.399749 0 #> [636,] 5.801023 0 #> [637,] 7.265691 0 #> [638,] 5.370188 0 #> [639,] 4.894873 0 #> [640,] 5.493457 0 #> [641,] 5.734250 0 #> [642,] 4.930295 0 #> [643,] 3.808431 0 #> [644,] 5.976408 0 #> [645,] 4.605815 0 #> [646,] 4.770862 0 #> [647,] 5.406588 0 #> [648,] 5.105629 0 #> [649,] 4.635775 0 #> [650,] 6.140334 0 #> [651,] 4.845058 0 #> [652,] 4.863513 0 #> [653,] 5.348916 0 #> [654,] 6.027477 0 #> [655,] 5.674001 0 #> [656,] 4.764467 0 #> [657,] 6.142376 0 #> [658,] 5.616845 0 #> [659,] 4.430326 0 #> [660,] 4.810077 0 #> [661,] 5.676379 0 #> [662,] 4.566416 0 #> [663,] 4.238390 0 #> [664,] 5.096798 0 #> [665,] 4.828042 0 #> [666,] 5.088630 0 #> [667,] 4.010339 0 #> [668,] 4.288057 0 #> [669,] 5.211723 0 #> [670,] 4.968670 0 #> [671,] 2.766377 0 #> [672,] 5.870460 0 #> [673,] 5.358856 0 #> [674,] 4.715795 0 #> [675,] 4.969381 0 #> [676,] 5.061035 0 #> [677,] 6.669621 0 #> [678,] 5.697250 0 #> [679,] 5.403520 0 #> [680,] 4.633642 0 #> [681,] 5.471435 0 #> [682,] 5.537645 0 #> [683,] 4.226920 0 #> [684,] 5.837938 0 #> [685,] 5.897236 0 #> [686,] 4.352410 0 #> [687,] 4.441955 0 #> [688,] 4.034040 0 #> [689,] 5.304813 0 #> [690,] 5.464001 0 #> [691,] 5.434683 0 #> [692,] 4.740093 0 #> [693,] 5.848070 0 #> [694,] 4.469613 0 #> [695,] 5.295348 0 #> [696,] 5.495367 0 #> [697,] 4.533725 0 #> [698,] 4.488026 0 #> [699,] 4.579108 0 #> [700,] 5.184222 0 #> [701,] 5.532139 0 #> [702,] 5.348684 0 #> [703,] 4.857925 0 #> [704,] 4.428535 0 #> [705,] 4.961068 0 #> [706,] 4.171562 0 #> [707,] 4.822307 0 #> [708,] 4.816405 0 #> [709,] 5.906019 0 #> [710,] 4.243828 0 #> [711,] 5.204671 0 #> [712,] 4.472506 0 #> [713,] 6.428801 0 #> [714,] 5.084785 0 #> [715,] 6.088572 0 #> [716,] 6.576893 0 #> [717,] 5.205091 0 #> [718,] 5.799816 0 #> [719,] 6.121609 0 #> [720,] 4.649332 0 #> [721,] 5.361074 0 #> [722,] 5.074634 0 #> [723,] 6.252053 0 #> [724,] 4.302907 0 #> [725,] 5.798890 0 #> 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[332,] 1.545657e-04 0 #> [333,] 4.808882e-04 0 #> [334,] 2.415266e-04 0 #> [335,] 3.091395e-04 0 #> [336,] 9.837649e-05 0 #> [337,] 1.975199e-04 0 #> [338,] 1.425942e-04 0 #> [339,] 2.957010e-04 0 #> [340,] 2.272218e-04 0 #> [341,] 3.809231e-04 0 #> [342,] 2.747640e-04 0 #> [343,] 2.403698e-04 0 #> [344,] 1.318787e-04 0 #> [345,] 1.312128e-04 0 #> [346,] 3.197906e-04 0 #> [347,] 3.511751e-04 0 #> [348,] 2.212144e-04 0 #> [349,] 2.117912e-04 0 #> [350,] 2.427150e-04 0 #> [351,] 4.194031e-04 0 #> [352,] 2.017630e-04 0 #> [353,] 3.337610e-04 0 #> [354,] 3.366892e-04 0 #> [355,] 1.869612e-04 0 #> [356,] 1.783136e-04 0 #> [357,] 2.761644e-04 0 #> [358,] 4.404260e-04 0 #> [359,] 3.888548e-04 0 #> [360,] 3.346423e-04 0 #> [361,] 3.095410e-04 0 #> [362,] 3.705765e-04 0 #> [363,] 2.982045e-04 0 #> [364,] 2.365162e-04 0 #> [365,] 1.468685e-04 0 #> [366,] 1.060493e-04 0 #> [367,] 3.314899e-04 0 #> [368,] 2.142466e-04 0 #> [369,] 2.360436e-04 0 #> [370,] 2.467690e-04 0 #> [371,] 3.199588e-04 0 #> [372,] 2.724874e-04 0 #> [373,] 2.098690e-04 0 #> [374,] 4.581940e-04 0 #> [375,] 3.303119e-04 0 #> [376,] 1.719382e-04 0 #> [377,] 3.018979e-04 0 #> [378,] 2.338475e-04 0 #> [379,] 3.392380e-04 0 #> [380,] 5.141703e-04 0 #> [381,] 1.597795e-04 0 #> [382,] 1.703533e-04 0 #> [383,] 3.063330e-04 0 #> [384,] 2.112797e-04 0 #> [385,] 2.104327e-04 0 #> [386,] 4.561084e-04 0 #> [387,] 3.546706e-04 0 #> [388,] 3.790253e-04 0 #> [389,] 1.851800e-04 0 #> [390,] 2.509202e-04 0 #> [391,] 9.791194e-05 0 #> [392,] 1.591114e-04 0 #> [393,] 1.587649e-04 0 #> [394,] 3.304360e-04 0 #> [395,] 2.151030e-04 0 #> [396,] 3.169665e-04 0 #> [397,] 2.097342e-04 0 #> [398,] 2.082469e-04 0 #> [399,] 2.514993e-04 0 #> [400,] 1.408431e-04 0 #> [401,] 3.708359e-04 0 #> [402,] 2.876342e-04 0 #> [403,] 2.910949e-04 0 #> [404,] 6.237237e-05 0 #> [405,] 4.700322e-04 0 #> [406,] 1.675128e-04 0 #> [407,] 2.893958e-04 0 #> [408,] 2.000427e-04 0 #> [409,] 1.777377e-04 0 #> [410,] 1.421449e-04 0 #> [411,] 1.987587e-04 0 #> [412,] 4.208507e-04 0 #> [413,] 1.095658e-04 0 #> [414,] 2.104461e-04 0 #> [415,] 1.105240e-04 0 #> [416,] 4.030025e-04 0 #> [417,] 1.739236e-04 0 #> [418,] 1.475393e-04 0 #> [419,] 2.220458e-04 0 #> [420,] 2.893314e-04 0 #> [421,] 1.133567e-04 0 #> [422,] 2.663714e-04 0 #> [423,] 2.177217e-04 0 #> [424,] 2.013209e-04 0 #> [425,] 3.802255e-04 0 #> [426,] 2.089242e-04 0 #> [427,] 1.594121e-04 0 #> [428,] 2.074647e-04 0 #> [429,] 1.056375e-04 0 #> [430,] 3.992094e-04 0 #> [431,] 2.957823e-04 0 #> [432,] 1.980855e-04 0 #> [433,] 3.552283e-04 0 #> [434,] 2.407333e-04 0 #> [435,] 2.424460e-04 0 #> [436,] 3.157091e-04 0 #> [437,] 2.653137e-04 0 #> [438,] 2.873695e-04 0 #> [439,] 3.297792e-04 0 #> [440,] 1.795462e-04 0 #> [441,] 2.284686e-04 0 #> [442,] 2.721920e-04 0 #> [443,] 2.982245e-04 0 #> [444,] 2.628720e-04 0 #> [445,] 2.660297e-04 0 #> [446,] 1.982129e-04 0 #> [447,] 2.945618e-04 0 #> [448,] 3.713204e-04 0 #> [449,] 3.222343e-04 0 #> [450,] 4.282617e-04 0 #> [451,] 2.885739e-04 0 #> [452,] 3.059782e-04 0 #> [453,] 3.139352e-04 0 #> [454,] 1.013619e-04 0 #> [455,] 1.398647e-04 0 #> [456,] 4.074999e-04 0 #> [457,] 3.180464e-04 0 #> [458,] 1.799393e-04 0 #> [459,] 2.573063e-04 0 #> [460,] 2.574127e-04 0 #> [461,] 3.837571e-04 0 #> [462,] 2.483195e-04 0 #> [463,] 8.844945e-05 0 #> [464,] 1.009246e-04 0 #> [465,] 3.486304e-04 0 #> [466,] 2.195502e-04 0 #> [467,] 1.673871e-04 0 #> [468,] 3.072224e-04 0 #> [469,] 2.509982e-04 0 #> [470,] 3.241261e-04 0 #> [471,] 3.760304e-04 0 #> [472,] 4.265878e-04 0 #> [473,] 3.503995e-04 0 #> [474,] 3.487650e-04 0 #> [475,] 2.108171e-04 0 #> [476,] 1.864104e-04 0 #> [477,] 2.565999e-04 0 #> [478,] 4.998003e-04 0 #> [479,] 3.242010e-04 0 #> [480,] 3.391160e-04 0 #> [481,] 6.542318e-04 0 #> [482,] 1.043097e-04 0 #> [483,] 1.886530e-04 0 #> [484,] 1.699839e-04 0 #> [485,] 9.744109e-05 0 #> [486,] 1.597365e-04 0 #> [487,] 2.268302e-04 0 #> [488,] 1.745874e-04 0 #> [489,] 1.721114e-04 0 #> [490,] 6.828457e-04 0 #> [491,] 4.083766e-04 0 #> [492,] 3.725653e-04 0 #> [493,] 4.448790e-04 0 #> [494,] 9.974543e-05 0 #> [495,] 3.921577e-04 0 #> [496,] 2.332768e-04 0 #> [497,] 2.503837e-04 0 #> [498,] 1.296128e-04 0 #> [499,] 2.816910e-04 0 #> [500,] 2.688046e-04 0 #> [501,] 4.084550e-04 0 #> [502,] 1.983299e-04 0 #> [503,] 3.195990e-04 0 #> [504,] 1.766680e-04 0 #> [505,] 1.488132e-04 0 #> [506,] 3.017238e-04 0 #> [507,] 4.497474e-04 0 #> [508,] 1.811854e-04 0 #> [509,] 2.822359e-04 0 #> [510,] 3.233739e-04 0 #> [511,] 2.266573e-04 0 #> [512,] 3.490566e-04 0 #> [513,] 2.538166e-04 0 #> [514,] 2.524564e-04 0 #> [515,] 4.379753e-04 0 #> [516,] 2.395782e-04 0 #> [517,] 1.451555e-04 0 #> [518,] 2.918630e-04 0 #> [519,] 4.319157e-04 0 #> [520,] 1.449371e-04 0 #> [521,] 2.408186e-04 0 #> [522,] 2.159483e-04 0 #> [523,] 1.493342e-04 0 #> [524,] 2.522421e-04 0 #> [525,] 1.280854e-04 0 #> [526,] 2.202640e-04 0 #> [527,] 1.401000e-04 0 #> [528,] 2.252545e-04 0 #> [529,] 4.192547e-04 0 #> [530,] 5.856150e-05 0 #> [531,] 2.303700e-04 0 #> [532,] 1.893912e-04 0 #> [533,] 2.950516e-04 0 #> [534,] 2.922270e-04 0 #> [535,] 2.112474e-04 0 #> [536,] 2.048496e-04 0 #> [537,] 2.996888e-04 0 #> [538,] 4.096615e-04 0 #> [539,] 2.214818e-04 0 #> [540,] 7.943973e-05 0 #> [541,] 2.351309e-04 0 #> [542,] 1.932919e-04 0 #> [543,] 4.004713e-04 0 #> [544,] 4.679694e-04 0 #> [545,] 1.740668e-04 0 #> [546,] 3.057172e-04 0 #> [547,] 2.849620e-04 0 #> [548,] 3.152946e-04 0 #> [549,] 4.264678e-04 0 #> [550,] 1.693906e-04 0 #> [551,] 1.516689e-04 0 #> [552,] 2.451270e-04 0 #> [553,] 3.815506e-04 0 #> [554,] 9.082806e-05 0 #> [555,] 3.713872e-04 0 #> [556,] 2.938459e-04 0 #> [557,] 2.234872e-04 0 #> [558,] 3.254973e-04 0 #> [559,] 2.948301e-04 0 #> [560,] 1.964489e-04 0 #> [561,] 2.279314e-04 0 #> [562,] 2.139851e-04 0 #> [563,] 4.205340e-04 0 #> [564,] 1.684487e-04 0 #> [565,] 8.652966e-05 0 #> [566,] 2.182924e-04 0 #> [567,] 3.284979e-04 0 #> [568,] 2.096685e-04 0 #> [569,] 2.649464e-04 0 #> [570,] 1.787032e-04 0 #> [571,] 2.014820e-04 0 #> [572,] 1.812511e-04 0 #> [573,] 3.124171e-04 0 #> [574,] 2.313510e-04 0 #> [575,] 4.504173e-04 0 #> [576,] 4.010612e-04 0 #> [577,] 3.200226e-04 0 #> [578,] 2.361785e-04 0 #> [579,] 2.522588e-04 0 #> [580,] 1.895863e-04 0 #> [581,] 4.105023e-04 0 #> [582,] 3.295778e-04 0 #> [583,] 4.747489e-04 0 #> [584,] 1.860833e-04 0 #> [585,] 2.008891e-04 0 #> [586,] 3.391290e-04 0 #> [587,] 2.367515e-04 0 #> [588,] 2.384270e-04 0 #> [589,] 9.573496e-05 0 #> [590,] 1.394594e-04 0 #> [591,] 2.405136e-04 0 #> [592,] 2.639716e-04 0 #> [593,] 2.545882e-04 0 #> [594,] 3.801204e-04 0 #> [595,] 3.092436e-04 0 #> [596,] 3.139222e-04 0 #> [597,] 1.216780e-04 0 #> [598,] 2.801116e-04 0 #> [599,] 1.471358e-04 0 #> [600,] 2.763946e-04 0 #> [601,] 4.226094e-04 0 #> [602,] 3.462439e-04 0 #> [603,] 1.465718e-04 0 #> [604,] 1.547441e-04 0 #> [605,] 1.419883e-04 0 #> [606,] 8.878479e-05 0 #> [607,] 1.208120e-04 0 #> [608,] 3.455454e-04 0 #> [609,] 2.711902e-04 0 #> [610,] 3.651901e-04 0 #> [611,] 5.341934e-04 0 #> [612,] 4.194523e-04 0 #> [613,] 1.921831e-04 0 #> [614,] 2.283533e-04 0 #> [615,] 2.354304e-04 0 #> [616,] 2.660503e-04 0 #> [617,] 2.473591e-04 0 #> [618,] 1.785404e-04 0 #> [619,] 2.116709e-04 0 #> [620,] 2.088175e-04 0 #> [621,] 2.492634e-04 0 #> [622,] 2.931449e-04 0 #> [623,] 4.939881e-04 0 #> [624,] 3.211250e-04 0 #> [625,] 1.343169e-04 0 #> [626,] 2.262327e-04 0 #> [627,] 1.886007e-04 0 #> [628,] 1.270536e-04 0 #> [629,] 2.835931e-04 0 #> [630,] 1.673164e-04 0 #> [631,] 3.483030e-04 0 #> [632,] 2.561686e-04 0 #> [633,] 2.678034e-04 0 #> [634,] 4.027118e-04 0 #> [635,] 2.168023e-04 0 #> [636,] 1.561287e-04 0 #> [637,] 5.022265e-05 0 #> [638,] 2.188013e-04 0 #> [639,] 2.493922e-04 0 #> [640,] 2.036902e-04 0 #> [641,] 1.625068e-04 0 #> [642,] 2.533160e-04 0 #> [643,] 4.844577e-04 0 #> [644,] 1.084860e-04 0 #> [645,] 3.485843e-04 0 #> [646,] 2.637173e-04 0 #> [647,] 2.155776e-04 0 #> [648,] 2.408999e-04 0 #> [649,] 3.644609e-04 0 #> [650,] 1.095570e-04 0 #> [651,] 3.207649e-04 0 #> [652,] 3.182078e-04 0 #> [653,] 2.235293e-04 0 #> [654,] 1.450184e-04 0 #> [655,] 1.749741e-04 0 #> [656,] 2.935187e-04 0 #> [657,] 1.103301e-04 0 #> [658,] 1.877717e-04 0 #> [659,] 3.202909e-04 0 #> [660,] 2.819825e-04 0 #> [661,] 1.684689e-04 0 #> [662,] 3.581793e-04 0 #> [663,] 4.056299e-04 0 #> [664,] 2.285957e-04 0 #> [665,] 3.057567e-04 0 #> [666,] 2.526728e-04 0 #> [667,] 3.799451e-04 0 #> [668,] 3.432114e-04 0 #> [669,] 2.057191e-04 0 #> [670,] 2.542318e-04 0 #> [671,] 5.487688e-04 0 #> [672,] 1.459682e-04 0 #> [673,] 2.192567e-04 0 #> [674,] 2.841136e-04 0 #> [675,] 2.772054e-04 0 #> [676,] 2.507460e-04 0 #> [677,] 5.551042e-05 0 #> [678,] 1.756016e-04 0 #> [679,] 2.160522e-04 0 #> [680,] 3.379077e-04 0 #> [681,] 2.059498e-04 0 #> [682,] 1.934096e-04 0 #> [683,] 3.707621e-04 0 #> [684,] 1.606186e-04 0 #> [685,] 1.541098e-04 0 #> [686,] 4.069886e-04 0 #> [687,] 3.313717e-04 0 #> [688,] 3.822233e-04 0 #> [689,] 2.313821e-04 0 #> [690,] 2.059575e-04 0 #> [691,] 1.949583e-04 0 #> [692,] 2.480859e-04 0 #> [693,] 1.515401e-04 0 #> [694,] 3.304577e-04 0 #> [695,] 2.325727e-04 0 #> [696,] 2.035595e-04 0 #> [697,] 2.950899e-04 0 #> [698,] 3.682394e-04 0 #> [699,] 3.535984e-04 0 #> [700,] 2.454970e-04 0 #> [701,] 1.963656e-04 0 #> [702,] 2.244821e-04 0 #> [703,] 2.620653e-04 0 #> [704,] 2.970199e-04 0 #> [705,] 2.597082e-04 0 #> [706,] 4.604239e-04 0 #> [707,] 3.180046e-04 0 #> [708,] 2.867155e-04 0 #> [709,] 1.561886e-04 0 #> [710,] 3.509606e-04 0 #> [711,] 2.110855e-04 0 #> [712,] 3.836408e-04 0 #> [713,] 3.849819e-05 0 #> [714,] 2.698589e-04 0 #> [715,] 1.317456e-04 0 #> [716,] 7.755576e-05 0 #> [717,] 2.209131e-04 0 #> [718,] 1.587916e-04 0 #> [719,] 1.288492e-04 0 #> [720,] 3.760651e-04 0 #> [721,] 2.173689e-04 0 #> [722,] 2.521303e-04 0 #> [723,] 1.045320e-04 0 #> [724,] 3.309668e-04 0 #> [725,] 1.572216e-04 0 #> [726,] 2.228909e-04 0 #> [727,] 3.337844e-04 0 #> [728,] 1.053994e-04 0 #> [729,] 1.871177e-04 0 #> [730,] 2.518188e-04 0 #> [731,] 4.384977e-04 0 #> [732,] 5.243590e-04 0 #> [733,] 2.018604e-04 0 #> [734,] 2.395444e-04 0 #> [735,] 3.241952e-04 0 #> [736,] 1.130585e-04 0 #> [737,] 2.450688e-04 0 #> [738,] 2.493143e-05 0 #> [739,] 2.882067e-04 0 #> [740,] 4.044332e-04 0 #> [741,] 1.149805e-04 0 #> [742,] 3.730648e-04 0 #> [743,] 1.283070e-04 0 #> [744,] 3.997110e-04 0 #> [745,] 1.822030e-04 0 #> [746,] 2.716948e-04 0 #> [747,] 1.137194e-04 0 #> [748,] 3.509345e-04 0 #> [749,] 2.534691e-04 0 #> [750,] 1.623516e-04 0 #> [751,] 3.234420e-04 0 #> [752,] 1.390159e-04 0 #> [753,] 3.052435e-04 0 #> [754,] 2.163745e-04 0 #> [755,] 1.824000e-04 0 #> [756,] 2.204347e-04 0 #> [757,] 2.936416e-04 0 #> [758,] 2.374341e-04 0 #> [759,] 1.472234e-04 0 #> [760,] 4.303334e-04 0 #> [761,] 3.104332e-04 0 #> [762,] 3.033111e-04 0 #> [763,] 2.722330e-04 0 #> [764,] 3.945617e-04 0 #> [765,] 2.630174e-04 0 #> [766,] 1.966927e-04 0 #> [767,] 3.844374e-04 0 #> [768,] 3.157664e-04 0 #> [769,] 4.335259e-05 0 #> [770,] 3.141318e-04 0 #> [771,] 1.654519e-04 0 #> [772,] 3.228501e-04 0 #> [773,] 3.028235e-04 0 #> [774,] 2.356557e-04 0 #> [775,] 2.774814e-04 0 #> [776,] 2.157424e-04 0 #> [777,] 1.091757e-04 0 #> [778,] 8.044973e-05 0 #> [779,] 8.345155e-05 0 #> [780,] 1.081871e-04 0 #> [781,] 2.364508e-04 0 #> [782,] 1.332829e-04 0 #> [783,] 2.488744e-04 0 #> [784,] 1.152727e-04 0 #> [785,] 2.535754e-04 0 #> [786,] 3.100772e-04 0 #> [787,] 4.785662e-04 0 #> [788,] 3.031556e-04 0 #> [789,] 2.736506e-04 0 #> [790,] 1.996877e-04 0 #> [791,] 1.275504e-04 0 #> [792,] 1.853950e-04 0 #> [793,] 1.734803e-04 0 #> [794,] 2.672030e-04 0 #> [795,] 7.667651e-05 0 #> [796,] 2.112453e-04 0 #> [797,] 3.736929e-04 0 #> [798,] 5.560811e-04 0 #> [799,] 1.790343e-04 0 #> [800,] 1.746465e-04 0 #> [801,] 2.447328e-04 0 #> [802,] 1.693475e-04 0 #> [803,] 1.674992e-04 0 #> [804,] 6.930171e-05 0 #> [805,] 8.983587e-05 0 #> [806,] 2.634435e-04 0 #> [807,] 2.582486e-04 0 #> [808,] 2.556380e-04 0 #> [809,] 1.995702e-04 0 #> [810,] 2.323220e-04 0 #> [811,] 2.857051e-04 0 #> [812,] 1.570781e-04 0 #> [813,] 2.510821e-04 0 #> [814,] 1.558716e-04 0 #> [815,] 2.963195e-04 0 #> [816,] 4.929957e-05 0 #> [817,] 2.629739e-04 0 #> [818,] 3.690959e-04 0 #> [819,] 3.209644e-04 0 #> [820,] 5.537454e-04 0 #> [821,] 1.403553e-04 0 #> [822,] 1.646922e-04 0 #> [823,] 4.237362e-04 0 #> [824,] 2.988110e-04 0 #> [825,] 3.215684e-04 0 #> [826,] 2.762804e-04 0 #> [827,] 1.348681e-04 0 #> [828,] 3.215862e-04 0 #> [829,] 1.280123e-04 0 #> [830,] 1.836707e-04 0 #> [831,] 3.014668e-04 0 #> [832,] 2.678450e-04 0 #> [833,] 3.514266e-04 0 #> [834,] 1.878796e-04 0 #> [835,] 2.105712e-04 0 #> [836,] 3.305988e-04 0 #> [837,] 6.751337e-05 0 #> [838,] 3.685690e-04 0 #> [839,] 1.003287e-04 0 #> [840,] 1.851440e-04 0 #> [841,] 1.603199e-04 0 #> [842,] 1.468121e-04 0 #> [843,] 3.060096e-04 0 #> [844,] 3.520104e-04 0 #> [845,] 5.563269e-04 0 #> [846,] 1.859809e-04 0 #> [847,] 1.993243e-04 0 #> [848,] 4.688514e-04 0 #> [849,] 2.369772e-04 0 #> [850,] 3.941052e-04 0 #> [851,] 1.504409e-04 0 #> [852,] 2.960572e-04 0 #> [853,] 2.108035e-04 0 #> [854,] 2.246083e-04 0 #> [855,] 2.852168e-04 0 #> [856,] 1.423209e-04 0 #> [857,] 3.518317e-04 0 #> [858,] 2.797742e-04 0 #> [859,] 2.222195e-04 0 #> [860,] 2.281903e-04 0 #> [861,] 1.316371e-04 0 #> [862,] 3.027514e-04 0 #> [863,] 2.942336e-04 0 #> [864,] 3.196133e-04 0 #> [865,] 2.178491e-04 0 #> [866,] 3.078019e-04 0 #> [867,] 1.659707e-04 0 #> [868,] 1.574956e-04 0 #> [869,] 2.561883e-04 0 #> [870,] 3.813491e-04 0 #> [871,] 1.841328e-04 0 #> [872,] 1.878828e-04 0 #> [873,] 2.876409e-04 0 #> [874,] 2.834307e-04 0 #> [875,] 2.445162e-04 0 #> [876,] 1.625989e-04 0 #> [877,] 3.593802e-04 0 #> [878,] 1.990678e-04 0 #> [879,] 1.946178e-04 0 #> [880,] 1.917611e-04 0 #> [881,] 1.932140e-04 0 #> [882,] 2.130751e-04 0 #> [883,] 2.530181e-04 0 #> [884,] 1.422756e-04 0 #> [885,] 2.391618e-04 0 #> [886,] 2.545553e-04 0 #> [887,] 2.307330e-04 0 #> [888,] 7.217122e-05 0 #> [889,] 4.158472e-04 0 #> [890,] 1.999728e-04 0 #> [891,] 2.276761e-04 0 #> [892,] 3.742574e-04 0 #> [893,] 2.460132e-04 0 #> [894,] 2.837380e-04 0 #> [895,] 2.418482e-04 0 #> [896,] 4.452901e-04 0 #> [897,] 2.472185e-04 0 #> [898,] 1.484630e-04 0 #> [899,] 2.565810e-04 0 #> [900,] 3.116468e-04 0 #> [901,] 2.057377e-04 0 #> [902,] 2.248702e-04 0 #> [903,] 2.476694e-04 0 #> [904,] 2.393110e-04 0 #> [905,] 1.758681e-04 0 #> [906,] 3.539754e-04 0 #> [907,] 2.837831e-04 0 #> [908,] 2.018909e-04 0 #> [909,] 2.859341e-04 0 #> [910,] 1.315055e-04 0 #> [911,] 3.526117e-04 0 #> [912,] 1.404419e-04 0 #> [913,] 1.636013e-04 0 #> [914,] 3.183048e-04 0 #> [915,] 1.109403e-04 0 #> [916,] 2.805868e-04 0 #> [917,] 4.248834e-05 0 #> [918,] 2.641218e-04 0 #> [919,] 1.591183e-04 0 #> [920,] 5.045417e-04 0 #> [921,] 1.781811e-04 0 #> [922,] 4.753054e-04 0 #> [923,] 2.873764e-04 0 #> [924,] 2.030911e-04 0 #> [925,] 1.672600e-04 0 #> [926,] 2.856257e-04 0 #> [927,] 4.057556e-04 0 #> [928,] 2.847504e-04 0 #> [929,] 1.221909e-04 0 #> [930,] 2.410461e-04 0 #> [931,] 1.670921e-04 0 #> [932,] 2.497373e-04 0 #> [933,] 9.214140e-05 0 #> [934,] 5.533825e-04 0 #> [935,] 2.583420e-04 0 #> [936,] 1.731495e-04 0 #> [937,] 1.590001e-04 0 #> [938,] 2.612118e-04 0 #> [939,] 1.852760e-04 0 #> [940,] 3.501000e-04 0 #> [941,] 2.984257e-04 0 #> [942,] 2.376755e-04 0 #> [943,] 3.153702e-04 0 #> [944,] 3.773165e-04 0 #> [945,] 3.644880e-04 0 #> [946,] 2.543904e-04 0 #> [947,] 2.732824e-04 0 #> [948,] 2.906559e-04 0 #> [949,] 1.327584e-04 0 #> [950,] 3.356992e-04 0 #> [951,] 3.028427e-04 0 #> [952,] 1.893701e-04 0 #> [953,] 1.358843e-04 0 #> [954,] 7.866640e-05 0 #> [955,] 3.341807e-04 0 #> [956,] 2.154368e-04 0 #> [957,] 3.409731e-04 0 #> [958,] 4.846245e-04 0 #> [959,] 2.034264e-04 0 #> [960,] 2.416117e-04 0 #> [961,] 3.012140e-04 0 #> [962,] 4.464723e-04 0 #> [963,] 3.008839e-04 0 #> [964,] 2.210974e-04 0 #> [965,] 2.549875e-04 0 #> [966,] 2.730135e-04 0 #> [967,] 4.464490e-04 0 #> [968,] 3.302712e-04 0 #> [969,] 2.318027e-04 0 #> [970,] 3.287092e-04 0 #> [971,] 1.561973e-04 0 #> [972,] 2.966892e-04 0 #> [973,] 2.479396e-04 0 #> [974,] 3.828799e-04 0 #> [975,] 2.450978e-04 0 #> [976,] 1.585412e-04 0 #> [977,] 3.925257e-04 0 #> [978,] 1.406725e-04 0 #> [979,] 2.428278e-04 0 #> [980,] 1.963504e-04 0 #> [981,] 2.996787e-04 0 #> [982,] 2.225087e-04 0 #> [983,] 3.079070e-04 0 #> [984,] 2.144225e-04 0 #> [985,] 3.831465e-04 0 #> [986,] 2.136507e-04 0 #> [987,] 9.502069e-05 0 #> [988,] 2.147885e-04 0 #> [989,] 2.199898e-04 0 #> [990,] 1.990254e-04 0 #> [991,] 2.568355e-04 0 #> [992,] 2.764570e-04 0 #> [993,] 1.339279e-04 0 #> [994,] 2.515579e-04 0 #> [995,] 3.253564e-04 0 #> [996,] 3.340186e-04 0 #> [997,] 3.056571e-04 0 #> [998,] 3.111801e-04 0 #> [999,] 2.623249e-04 0 #> [1000,] 2.901752e-04 0 #> #> $select #> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 #> [15] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 #> [29] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 #> [43] 43 44 45 46 47 48 49 50 51 52 53 54 55 56 #> [57] 57 58 59 60 61 62 63 64 65 66 67 68 69 70 #> [71] 71 72 73 74 75 76 77 78 79 80 81 82 83 84 #> [85] 85 86 87 88 89 90 91 92 93 94 95 96 97 98 #> [99] 99 100 101 102 103 104 105 106 107 108 109 110 111 112 #> [113] 113 114 115 116 117 118 119 120 121 122 123 124 125 126 #> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 #> [141] 141 142 143 144 145 146 147 148 149 150 151 152 153 154 #> [155] 155 156 157 158 159 160 161 162 163 164 165 166 167 168 #> [169] 169 170 171 172 173 174 175 176 177 178 179 180 181 182 #> [183] 183 184 185 186 187 188 189 190 191 192 193 194 195 196 #> [197] 197 198 199 200 201 202 203 204 205 206 207 208 209 210 #> [211] 211 212 213 214 215 216 217 218 219 220 221 222 223 224 #> [225] 225 226 227 228 229 230 231 232 233 234 235 236 237 238 #> [239] 239 240 241 242 243 244 245 246 247 248 249 250 251 252 #> [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 #> [267] 267 268 269 270 271 272 273 274 275 276 277 278 279 280 #> [281] 281 282 283 284 285 286 287 288 289 290 291 292 293 294 #> [295] 295 296 297 298 299 300 301 302 303 304 305 306 307 308 #> [309] 309 310 311 312 313 314 315 316 317 318 319 320 321 322 #> [323] 323 324 325 326 327 328 329 330 331 332 333 334 335 336 #> [337] 337 338 339 340 341 342 343 344 345 346 347 348 349 350 #> [351] 351 352 353 354 355 356 357 358 359 360 361 362 363 364 #> [365] 365 366 367 368 369 370 371 372 373 374 375 376 377 378 #> [379] 379 380 381 382 383 384 385 386 387 388 389 390 391 392 #> [393] 393 394 395 396 397 398 399 400 401 402 403 404 405 406 #> [407] 407 408 409 410 411 412 413 414 415 416 417 418 419 420 #> [421] 421 422 423 424 425 426 427 428 429 430 431 432 433 434 #> [435] 435 436 437 438 439 440 441 442 443 444 445 446 447 448 #> [449] 449 450 451 452 453 454 455 456 457 458 459 460 461 462 #> [463] 463 464 465 466 467 468 469 470 471 472 473 474 475 476 #> [477] 477 478 479 480 481 482 483 484 485 486 487 488 489 490 #> [491] 491 492 493 494 495 496 497 498 499 500 501 502 503 504 #> [505] 505 506 507 508 509 510 511 512 513 514 515 516 517 518 #> [519] 519 520 521 522 523 524 525 526 527 528 529 530 531 532 #> [533] 533 534 535 536 537 538 539 540 541 542 543 544 545 546 #> [547] 547 548 549 550 551 552 553 554 555 556 557 558 559 560 #> [561] 561 562 563 564 565 566 567 568 569 570 571 572 573 574 #> [575] 575 576 577 578 579 580 581 582 583 584 585 586 587 588 #> [589] 589 590 591 592 593 594 595 596 597 598 599 600 601 602 #> [603] 603 604 605 606 607 608 609 610 611 612 613 614 615 616 #> [617] 617 618 619 620 621 622 623 624 625 626 627 628 629 630 #> [631] 631 632 633 634 635 636 637 638 639 640 641 642 643 644 #> [645] 645 646 647 648 649 650 651 652 653 654 655 656 657 658 #> [659] 659 660 661 662 663 664 665 666 667 668 669 670 671 672 #> [673] 673 674 675 676 677 678 679 680 681 682 683 684 685 686 #> [687] 687 688 689 690 691 692 693 694 695 696 697 698 699 700 #> [701] 701 702 703 704 705 706 707 708 709 710 711 712 713 714 #> [715] 715 716 717 718 719 720 721 722 723 724 725 726 727 728 #> [729] 729 730 731 732 733 734 735 736 737 738 739 740 741 742 #> [743] 743 744 745 746 747 748 749 750 751 752 753 754 755 756 #> [757] 757 758 759 760 761 762 763 764 765 766 767 768 769 770 #> [771] 771 772 773 774 775 776 777 778 779 780 781 782 783 784 #> [785] 785 786 787 788 789 790 791 792 793 794 795 796 797 798 #> [799] 799 800 801 802 803 804 805 806 807 808 809 810 811 812 #> [813] 813 814 815 816 817 818 819 820 821 822 823 824 825 826 #> [827] 827 828 829 830 831 832 833 834 835 836 837 838 839 840 #> [841] 841 842 843 844 845 846 847 848 849 850 851 852 853 854 #> [855] 855 856 857 858 859 860 861 862 863 864 865 866 867 868 #> [869] 869 870 871 872 873 874 875 876 877 878 879 880 881 882 #> [883] 883 884 885 886 887 888 889 890 891 892 893 894 895 896 #> [897] 897 898 899 900 901 902 903 904 905 906 907 908 909 910 #> [911] 911 912 913 914 915 916 917 918 919 920 921 922 923 924 #> [925] 925 926 927 928 929 930 931 932 933 934 935 936 937 938 #> [939] 939 940 941 942 943 944 945 946 947 948 949 950 951 952 #> [953] 953 954 955 956 957 958 959 960 961 962 963 964 965 966 #> [967] 967 968 969 970 971 972 973 974 975 976 977 978 979 980 #> [981] 981 982 983 984 985 986 987 988 989 990 991 992 993 994 #> [995] 995 996 997 998 999 1000 #> #> $formula #> [1] \"te( beta.1.,beta.2., bs='cr')\" #> #> $pars #> [1] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [5] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [9] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [13] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [17] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [21] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [25] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [29] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [33] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [37] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [41] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [45] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [49] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [53] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [57] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [61] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [65] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [69] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [73] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [77] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [81] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [85] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [89] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [93] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [97] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [101] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [105] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [109] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [113] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [117] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [121] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [125] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [129] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [133] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [137] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [141] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [145] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [149] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [153] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [157] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [161] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [165] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [169] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [173] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [177] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [181] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [185] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [189] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [193] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [197] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [201] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [205] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [209] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [213] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [217] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [221] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [225] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [229] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [233] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [237] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [241] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [245] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [249] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [253] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [257] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [261] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [265] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [269] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [273] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [277] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [281] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [285] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [289] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [293] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [297] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [301] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [305] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [309] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [313] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [317] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [321] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [325] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [329] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [333] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [337] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [341] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [345] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [349] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [353] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [357] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [361] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [365] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [369] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [373] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [377] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [381] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [385] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [389] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [393] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [397] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [401] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [405] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [409] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [413] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [417] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [421] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [425] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [429] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [433] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [437] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [441] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [445] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [449] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [453] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [457] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [461] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [465] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [469] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [473] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [477] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [481] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [485] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [489] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [493] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [497] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [501] \"beta.1.,beta.2.\" #> #> $res #> pars k evppi #> 1 beta.1.,beta.2. 0 0.000000e+00 #> 2 beta.1.,beta.2. 100 0.000000e+00 #> 3 beta.1.,beta.2. 200 0.000000e+00 #> 4 beta.1.,beta.2. 300 0.000000e+00 #> 5 beta.1.,beta.2. 400 0.000000e+00 #> 6 beta.1.,beta.2. 500 0.000000e+00 #> 7 beta.1.,beta.2. 600 0.000000e+00 #> 8 beta.1.,beta.2. 700 0.000000e+00 #> 9 beta.1.,beta.2. 800 0.000000e+00 #> 10 beta.1.,beta.2. 900 0.000000e+00 #> 11 beta.1.,beta.2. 1000 0.000000e+00 #> 12 beta.1.,beta.2. 1100 0.000000e+00 #> 13 beta.1.,beta.2. 1200 0.000000e+00 #> 14 beta.1.,beta.2. 1300 0.000000e+00 #> 15 beta.1.,beta.2. 1400 0.000000e+00 #> 16 beta.1.,beta.2. 1500 0.000000e+00 #> 17 beta.1.,beta.2. 1600 0.000000e+00 #> 18 beta.1.,beta.2. 1700 0.000000e+00 #> 19 beta.1.,beta.2. 1800 0.000000e+00 #> 20 beta.1.,beta.2. 1900 0.000000e+00 #> 21 beta.1.,beta.2. 2000 0.000000e+00 #> 22 beta.1.,beta.2. 2100 0.000000e+00 #> 23 beta.1.,beta.2. 2200 0.000000e+00 #> 24 beta.1.,beta.2. 2300 0.000000e+00 #> 25 beta.1.,beta.2. 2400 0.000000e+00 #> 26 beta.1.,beta.2. 2500 0.000000e+00 #> 27 beta.1.,beta.2. 2600 0.000000e+00 #> 28 beta.1.,beta.2. 2700 0.000000e+00 #> 29 beta.1.,beta.2. 2800 0.000000e+00 #> 30 beta.1.,beta.2. 2900 0.000000e+00 #> 31 beta.1.,beta.2. 3000 0.000000e+00 #> 32 beta.1.,beta.2. 3100 0.000000e+00 #> 33 beta.1.,beta.2. 3200 0.000000e+00 #> 34 beta.1.,beta.2. 3300 0.000000e+00 #> 35 beta.1.,beta.2. 3400 2.457218e-05 #> 36 beta.1.,beta.2. 3500 9.285676e-05 #> 37 beta.1.,beta.2. 3600 1.611413e-04 #> 38 beta.1.,beta.2. 3700 2.294259e-04 #> 39 beta.1.,beta.2. 3800 2.977105e-04 #> 40 beta.1.,beta.2. 3900 3.659951e-04 #> 41 beta.1.,beta.2. 4000 4.342796e-04 #> 42 beta.1.,beta.2. 4100 5.025642e-04 #> 43 beta.1.,beta.2. 4200 5.708488e-04 #> 44 beta.1.,beta.2. 4300 6.391333e-04 #> 45 beta.1.,beta.2. 4400 7.074179e-04 #> 46 beta.1.,beta.2. 4500 8.359579e-04 #> 47 beta.1.,beta.2. 4600 9.696656e-04 #> 48 beta.1.,beta.2. 4700 1.103373e-03 #> 49 beta.1.,beta.2. 4800 1.237081e-03 #> 50 beta.1.,beta.2. 4900 1.370789e-03 #> 51 beta.1.,beta.2. 5000 1.504497e-03 #> 52 beta.1.,beta.2. 5100 1.670549e-03 #> 53 beta.1.,beta.2. 5200 1.859133e-03 #> 54 beta.1.,beta.2. 5300 2.047718e-03 #> 55 beta.1.,beta.2. 5400 2.236302e-03 #> 56 beta.1.,beta.2. 5500 2.424887e-03 #> 57 beta.1.,beta.2. 5600 2.613472e-03 #> 58 beta.1.,beta.2. 5700 2.802056e-03 #> 59 beta.1.,beta.2. 5800 2.990641e-03 #> 60 beta.1.,beta.2. 5900 3.179226e-03 #> 61 beta.1.,beta.2. 6000 3.388697e-03 #> 62 beta.1.,beta.2. 6100 3.632914e-03 #> 63 beta.1.,beta.2. 6200 3.877131e-03 #> 64 beta.1.,beta.2. 6300 4.139327e-03 #> 65 beta.1.,beta.2. 6400 4.557299e-03 #> 66 beta.1.,beta.2. 6500 5.018772e-03 #> 67 beta.1.,beta.2. 6600 5.518913e-03 #> 68 beta.1.,beta.2. 6700 6.019054e-03 #> 69 beta.1.,beta.2. 6800 6.519195e-03 #> 70 beta.1.,beta.2. 6900 7.072839e-03 #> 71 beta.1.,beta.2. 7000 7.631472e-03 #> 72 beta.1.,beta.2. 7100 8.238981e-03 #> 73 beta.1.,beta.2. 7200 8.914107e-03 #> 74 beta.1.,beta.2. 7300 9.667252e-03 #> 75 beta.1.,beta.2. 7400 1.050133e-02 #> 76 beta.1.,beta.2. 7500 1.136910e-02 #> 77 beta.1.,beta.2. 7600 1.233047e-02 #> 78 beta.1.,beta.2. 7700 1.330277e-02 #> 79 beta.1.,beta.2. 7800 1.435958e-02 #> 80 beta.1.,beta.2. 7900 1.545712e-02 #> 81 beta.1.,beta.2. 8000 1.665844e-02 #> 82 beta.1.,beta.2. 8100 1.791987e-02 #> 83 beta.1.,beta.2. 8200 1.926646e-02 #> 84 beta.1.,beta.2. 8300 2.069755e-02 #> 85 beta.1.,beta.2. 8400 2.215719e-02 #> 86 beta.1.,beta.2. 8500 2.365858e-02 #> 87 beta.1.,beta.2. 8600 2.536231e-02 #> 88 beta.1.,beta.2. 8700 2.721777e-02 #> 89 beta.1.,beta.2. 8800 2.926248e-02 #> 90 beta.1.,beta.2. 8900 3.142167e-02 #> 91 beta.1.,beta.2. 9000 3.366613e-02 #> 92 beta.1.,beta.2. 9100 3.594727e-02 #> 93 beta.1.,beta.2. 9200 3.828947e-02 #> 94 beta.1.,beta.2. 9300 4.066173e-02 #> 95 beta.1.,beta.2. 9400 4.308486e-02 #> 96 beta.1.,beta.2. 9500 4.554495e-02 #> 97 beta.1.,beta.2. 9600 4.810142e-02 #> 98 beta.1.,beta.2. 9700 5.076759e-02 #> 99 beta.1.,beta.2. 9800 5.354944e-02 #> 100 beta.1.,beta.2. 9900 5.645627e-02 #> 101 beta.1.,beta.2. 10000 5.953500e-02 #> 102 beta.1.,beta.2. 10100 6.272953e-02 #> 103 beta.1.,beta.2. 10200 6.605710e-02 #> 104 beta.1.,beta.2. 10300 6.946561e-02 #> 105 beta.1.,beta.2. 10400 7.299518e-02 #> 106 beta.1.,beta.2. 10500 7.661519e-02 #> 107 beta.1.,beta.2. 10600 8.044882e-02 #> 108 beta.1.,beta.2. 10700 8.454452e-02 #> 109 beta.1.,beta.2. 10800 8.880028e-02 #> 110 beta.1.,beta.2. 10900 9.316356e-02 #> 111 beta.1.,beta.2. 11000 9.757211e-02 #> 112 beta.1.,beta.2. 11100 1.020631e-01 #> 113 beta.1.,beta.2. 11200 1.066899e-01 #> 114 beta.1.,beta.2. 11300 1.115583e-01 #> 115 beta.1.,beta.2. 11400 1.165761e-01 #> 116 beta.1.,beta.2. 11500 1.217236e-01 #> 117 beta.1.,beta.2. 11600 1.269848e-01 #> 118 beta.1.,beta.2. 11700 1.324089e-01 #> 119 beta.1.,beta.2. 11800 1.379159e-01 #> 120 beta.1.,beta.2. 11900 1.435288e-01 #> 121 beta.1.,beta.2. 12000 1.492557e-01 #> 122 beta.1.,beta.2. 12100 1.550713e-01 #> 123 beta.1.,beta.2. 12200 1.610079e-01 #> 124 beta.1.,beta.2. 12300 1.670532e-01 #> 125 beta.1.,beta.2. 12400 1.733199e-01 #> 126 beta.1.,beta.2. 12500 1.797311e-01 #> 127 beta.1.,beta.2. 12600 1.862892e-01 #> 128 beta.1.,beta.2. 12700 1.929569e-01 #> 129 beta.1.,beta.2. 12800 1.998673e-01 #> 130 beta.1.,beta.2. 12900 2.069650e-01 #> 131 beta.1.,beta.2. 13000 2.141277e-01 #> 132 beta.1.,beta.2. 13100 2.214818e-01 #> 133 beta.1.,beta.2. 13200 2.290093e-01 #> 134 beta.1.,beta.2. 13300 2.366830e-01 #> 135 beta.1.,beta.2. 13400 2.444225e-01 #> 136 beta.1.,beta.2. 13500 2.522186e-01 #> 137 beta.1.,beta.2. 13600 2.601369e-01 #> 138 beta.1.,beta.2. 13700 2.681379e-01 #> 139 beta.1.,beta.2. 13800 2.761958e-01 #> 140 beta.1.,beta.2. 13900 2.844056e-01 #> 141 beta.1.,beta.2. 14000 2.927010e-01 #> 142 beta.1.,beta.2. 14100 3.011088e-01 #> 143 beta.1.,beta.2. 14200 3.096150e-01 #> 144 beta.1.,beta.2. 14300 3.182103e-01 #> 145 beta.1.,beta.2. 14400 3.270021e-01 #> 146 beta.1.,beta.2. 14500 3.359932e-01 #> 147 beta.1.,beta.2. 14600 3.451267e-01 #> 148 beta.1.,beta.2. 14700 3.543843e-01 #> 149 beta.1.,beta.2. 14800 3.637561e-01 #> 150 beta.1.,beta.2. 14900 3.733013e-01 #> 151 beta.1.,beta.2. 15000 3.829607e-01 #> 152 beta.1.,beta.2. 15100 3.927341e-01 #> 153 beta.1.,beta.2. 15200 4.026574e-01 #> 154 beta.1.,beta.2. 15300 4.127028e-01 #> 155 beta.1.,beta.2. 15400 4.228980e-01 #> 156 beta.1.,beta.2. 15500 4.332371e-01 #> 157 beta.1.,beta.2. 15600 4.437379e-01 #> 158 beta.1.,beta.2. 15700 4.544137e-01 #> 159 beta.1.,beta.2. 15800 4.651813e-01 #> 160 beta.1.,beta.2. 15900 4.761334e-01 #> 161 beta.1.,beta.2. 16000 4.872191e-01 #> 162 beta.1.,beta.2. 16100 4.984766e-01 #> 163 beta.1.,beta.2. 16200 5.098566e-01 #> 164 beta.1.,beta.2. 16300 5.214073e-01 #> 165 beta.1.,beta.2. 16400 5.330834e-01 #> 166 beta.1.,beta.2. 16500 5.449266e-01 #> 167 beta.1.,beta.2. 16600 5.569304e-01 #> 168 beta.1.,beta.2. 16700 5.690002e-01 #> 169 beta.1.,beta.2. 16800 5.812412e-01 #> 170 beta.1.,beta.2. 16900 5.936338e-01 #> 171 beta.1.,beta.2. 17000 6.061169e-01 #> 172 beta.1.,beta.2. 17100 6.187243e-01 #> 173 beta.1.,beta.2. 17200 6.314361e-01 #> 174 beta.1.,beta.2. 17300 6.442246e-01 #> 175 beta.1.,beta.2. 17400 6.570961e-01 #> 176 beta.1.,beta.2. 17500 6.700288e-01 #> 177 beta.1.,beta.2. 17600 6.830269e-01 #> 178 beta.1.,beta.2. 17700 6.960982e-01 #> 179 beta.1.,beta.2. 17800 7.092648e-01 #> 180 beta.1.,beta.2. 17900 7.225512e-01 #> 181 beta.1.,beta.2. 18000 7.358751e-01 #> 182 beta.1.,beta.2. 18100 7.492499e-01 #> 183 beta.1.,beta.2. 18200 7.627448e-01 #> 184 beta.1.,beta.2. 18300 7.763942e-01 #> 185 beta.1.,beta.2. 18400 7.901216e-01 #> 186 beta.1.,beta.2. 18500 8.039977e-01 #> 187 beta.1.,beta.2. 18600 8.179865e-01 #> 188 beta.1.,beta.2. 18700 8.320791e-01 #> 189 beta.1.,beta.2. 18800 8.462536e-01 #> 190 beta.1.,beta.2. 18900 8.605366e-01 #> 191 beta.1.,beta.2. 19000 8.749299e-01 #> 192 beta.1.,beta.2. 19100 8.893719e-01 #> 193 beta.1.,beta.2. 19200 9.039347e-01 #> 194 beta.1.,beta.2. 19300 9.186171e-01 #> 195 beta.1.,beta.2. 19400 9.334328e-01 #> 196 beta.1.,beta.2. 19500 9.483216e-01 #> 197 beta.1.,beta.2. 19600 9.632936e-01 #> 198 beta.1.,beta.2. 19700 9.783535e-01 #> 199 beta.1.,beta.2. 19800 9.935304e-01 #> 200 beta.1.,beta.2. 19900 1.008831e+00 #> 201 beta.1.,beta.2. 20000 1.024234e+00 #> 202 beta.1.,beta.2. 20100 1.039745e+00 #> 203 beta.1.,beta.2. 20200 1.055422e+00 #> 204 beta.1.,beta.2. 20300 1.071224e+00 #> 205 beta.1.,beta.2. 20400 1.084066e+00 #> 206 beta.1.,beta.2. 20500 1.074809e+00 #> 207 beta.1.,beta.2. 20600 1.065660e+00 #> 208 beta.1.,beta.2. 20700 1.056641e+00 #> 209 beta.1.,beta.2. 20800 1.047729e+00 #> 210 beta.1.,beta.2. 20900 1.038873e+00 #> 211 beta.1.,beta.2. 21000 1.030071e+00 #> 212 beta.1.,beta.2. 21100 1.021314e+00 #> 213 beta.1.,beta.2. 21200 1.012653e+00 #> 214 beta.1.,beta.2. 21300 1.004120e+00 #> 215 beta.1.,beta.2. 21400 9.956920e-01 #> 216 beta.1.,beta.2. 21500 9.872935e-01 #> 217 beta.1.,beta.2. 21600 9.789248e-01 #> 218 beta.1.,beta.2. 21700 9.706356e-01 #> 219 beta.1.,beta.2. 21800 9.623851e-01 #> 220 beta.1.,beta.2. 21900 9.542037e-01 #> 221 beta.1.,beta.2. 22000 9.461404e-01 #> 222 beta.1.,beta.2. 22100 9.381714e-01 #> 223 beta.1.,beta.2. 22200 9.302605e-01 #> 224 beta.1.,beta.2. 22300 9.224375e-01 #> 225 beta.1.,beta.2. 22400 9.147457e-01 #> 226 beta.1.,beta.2. 22500 9.071125e-01 #> 227 beta.1.,beta.2. 22600 8.995306e-01 #> 228 beta.1.,beta.2. 22700 8.919891e-01 #> 229 beta.1.,beta.2. 22800 8.844713e-01 #> 230 beta.1.,beta.2. 22900 8.770460e-01 #> 231 beta.1.,beta.2. 23000 8.697233e-01 #> 232 beta.1.,beta.2. 23100 8.625451e-01 #> 233 beta.1.,beta.2. 23200 8.554401e-01 #> 234 beta.1.,beta.2. 23300 8.483837e-01 #> 235 beta.1.,beta.2. 23400 8.413724e-01 #> 236 beta.1.,beta.2. 23500 8.344054e-01 #> 237 beta.1.,beta.2. 23600 8.275071e-01 #> 238 beta.1.,beta.2. 23700 8.206365e-01 #> 239 beta.1.,beta.2. 23800 8.138200e-01 #> 240 beta.1.,beta.2. 23900 8.071060e-01 #> 241 beta.1.,beta.2. 24000 8.004723e-01 #> 242 beta.1.,beta.2. 24100 7.938925e-01 #> 243 beta.1.,beta.2. 24200 7.873783e-01 #> 244 beta.1.,beta.2. 24300 7.809368e-01 #> 245 beta.1.,beta.2. 24400 7.745497e-01 #> 246 beta.1.,beta.2. 24500 7.682354e-01 #> 247 beta.1.,beta.2. 24600 7.619523e-01 #> 248 beta.1.,beta.2. 24700 7.557112e-01 #> 249 beta.1.,beta.2. 24800 7.495586e-01 #> 250 beta.1.,beta.2. 24900 7.434515e-01 #> 251 beta.1.,beta.2. 25000 7.374292e-01 #> 252 beta.1.,beta.2. 25100 7.314887e-01 #> 253 beta.1.,beta.2. 25200 7.256113e-01 #> 254 beta.1.,beta.2. 25300 7.197572e-01 #> 255 beta.1.,beta.2. 25400 7.139554e-01 #> 256 beta.1.,beta.2. 25500 7.081910e-01 #> 257 beta.1.,beta.2. 25600 7.024605e-01 #> 258 beta.1.,beta.2. 25700 6.967964e-01 #> 259 beta.1.,beta.2. 25800 6.912702e-01 #> 260 beta.1.,beta.2. 25900 6.858125e-01 #> 261 beta.1.,beta.2. 26000 6.804006e-01 #> 262 beta.1.,beta.2. 26100 6.750311e-01 #> 263 beta.1.,beta.2. 26200 6.697037e-01 #> 264 beta.1.,beta.2. 26300 6.644020e-01 #> 265 beta.1.,beta.2. 26400 6.591274e-01 #> 266 beta.1.,beta.2. 26500 6.539432e-01 #> 267 beta.1.,beta.2. 26600 6.488189e-01 #> 268 beta.1.,beta.2. 26700 6.437494e-01 #> 269 beta.1.,beta.2. 26800 6.387192e-01 #> 270 beta.1.,beta.2. 26900 6.337315e-01 #> 271 beta.1.,beta.2. 27000 6.287676e-01 #> 272 beta.1.,beta.2. 27100 6.238712e-01 #> 273 beta.1.,beta.2. 27200 6.190358e-01 #> 274 beta.1.,beta.2. 27300 6.143003e-01 #> 275 beta.1.,beta.2. 27400 6.096329e-01 #> 276 beta.1.,beta.2. 27500 6.050127e-01 #> 277 beta.1.,beta.2. 27600 6.004741e-01 #> 278 beta.1.,beta.2. 27700 5.960047e-01 #> 279 beta.1.,beta.2. 27800 5.915586e-01 #> 280 beta.1.,beta.2. 27900 5.871403e-01 #> 281 beta.1.,beta.2. 28000 5.827532e-01 #> 282 beta.1.,beta.2. 28100 5.783884e-01 #> 283 beta.1.,beta.2. 28200 5.740675e-01 #> 284 beta.1.,beta.2. 28300 5.697920e-01 #> 285 beta.1.,beta.2. 28400 5.655458e-01 #> 286 beta.1.,beta.2. 28500 5.613352e-01 #> 287 beta.1.,beta.2. 28600 5.571441e-01 #> 288 beta.1.,beta.2. 28700 5.529855e-01 #> 289 beta.1.,beta.2. 28800 5.488534e-01 #> 290 beta.1.,beta.2. 28900 5.447722e-01 #> 291 beta.1.,beta.2. 29000 5.406968e-01 #> 292 beta.1.,beta.2. 29100 5.366213e-01 #> 293 beta.1.,beta.2. 29200 5.325726e-01 #> 294 beta.1.,beta.2. 29300 5.285423e-01 #> 295 beta.1.,beta.2. 29400 5.245240e-01 #> 296 beta.1.,beta.2. 29500 5.205150e-01 #> 297 beta.1.,beta.2. 29600 5.165845e-01 #> 298 beta.1.,beta.2. 29700 5.126795e-01 #> 299 beta.1.,beta.2. 29800 5.088076e-01 #> 300 beta.1.,beta.2. 29900 5.049948e-01 #> 301 beta.1.,beta.2. 30000 5.012110e-01 #> 302 beta.1.,beta.2. 30100 4.975225e-01 #> 303 beta.1.,beta.2. 30200 4.938624e-01 #> 304 beta.1.,beta.2. 30300 4.902186e-01 #> 305 beta.1.,beta.2. 30400 4.865747e-01 #> 306 beta.1.,beta.2. 30500 4.829573e-01 #> 307 beta.1.,beta.2. 30600 4.794062e-01 #> 308 beta.1.,beta.2. 30700 4.758743e-01 #> 309 beta.1.,beta.2. 30800 4.723592e-01 #> 310 beta.1.,beta.2. 30900 4.688739e-01 #> 311 beta.1.,beta.2. 31000 4.654132e-01 #> 312 beta.1.,beta.2. 31100 4.619793e-01 #> 313 beta.1.,beta.2. 31200 4.585647e-01 #> 314 beta.1.,beta.2. 31300 4.551584e-01 #> 315 beta.1.,beta.2. 31400 4.517521e-01 #> 316 beta.1.,beta.2. 31500 4.483486e-01 #> 317 beta.1.,beta.2. 31600 4.449656e-01 #> 318 beta.1.,beta.2. 31700 4.416163e-01 #> 319 beta.1.,beta.2. 31800 4.383141e-01 #> 320 beta.1.,beta.2. 31900 4.350518e-01 #> 321 beta.1.,beta.2. 32000 4.318246e-01 #> 322 beta.1.,beta.2. 32100 4.286196e-01 #> 323 beta.1.,beta.2. 32200 4.254631e-01 #> 324 beta.1.,beta.2. 32300 4.223070e-01 #> 325 beta.1.,beta.2. 32400 4.191951e-01 #> 326 beta.1.,beta.2. 32500 4.161302e-01 #> 327 beta.1.,beta.2. 32600 4.131096e-01 #> 328 beta.1.,beta.2. 32700 4.101110e-01 #> 329 beta.1.,beta.2. 32800 4.071347e-01 #> 330 beta.1.,beta.2. 32900 4.041827e-01 #> 331 beta.1.,beta.2. 33000 4.012589e-01 #> 332 beta.1.,beta.2. 33100 3.983743e-01 #> 333 beta.1.,beta.2. 33200 3.955212e-01 #> 334 beta.1.,beta.2. 33300 3.927254e-01 #> 335 beta.1.,beta.2. 33400 3.899432e-01 #> 336 beta.1.,beta.2. 33500 3.871855e-01 #> 337 beta.1.,beta.2. 33600 3.844689e-01 #> 338 beta.1.,beta.2. 33700 3.817796e-01 #> 339 beta.1.,beta.2. 33800 3.791394e-01 #> 340 beta.1.,beta.2. 33900 3.765335e-01 #> 341 beta.1.,beta.2. 34000 3.739600e-01 #> 342 beta.1.,beta.2. 34100 3.714164e-01 #> 343 beta.1.,beta.2. 34200 3.688934e-01 #> 344 beta.1.,beta.2. 34300 3.663704e-01 #> 345 beta.1.,beta.2. 34400 3.638535e-01 #> 346 beta.1.,beta.2. 34500 3.613474e-01 #> 347 beta.1.,beta.2. 34600 3.588413e-01 #> 348 beta.1.,beta.2. 34700 3.563576e-01 #> 349 beta.1.,beta.2. 34800 3.539089e-01 #> 350 beta.1.,beta.2. 34900 3.514843e-01 #> 351 beta.1.,beta.2. 35000 3.490616e-01 #> 352 beta.1.,beta.2. 35100 3.466548e-01 #> 353 beta.1.,beta.2. 35200 3.442652e-01 #> 354 beta.1.,beta.2. 35300 3.418779e-01 #> 355 beta.1.,beta.2. 35400 3.395314e-01 #> 356 beta.1.,beta.2. 35500 3.372204e-01 #> 357 beta.1.,beta.2. 35600 3.349244e-01 #> 358 beta.1.,beta.2. 35700 3.326430e-01 #> 359 beta.1.,beta.2. 35800 3.303822e-01 #> 360 beta.1.,beta.2. 35900 3.281223e-01 #> 361 beta.1.,beta.2. 36000 3.258784e-01 #> 362 beta.1.,beta.2. 36100 3.236347e-01 #> 363 beta.1.,beta.2. 36200 3.214071e-01 #> 364 beta.1.,beta.2. 36300 3.191795e-01 #> 365 beta.1.,beta.2. 36400 3.169807e-01 #> 366 beta.1.,beta.2. 36500 3.148009e-01 #> 367 beta.1.,beta.2. 36600 3.126353e-01 #> 368 beta.1.,beta.2. 36700 3.105210e-01 #> 369 beta.1.,beta.2. 36800 3.084279e-01 #> 370 beta.1.,beta.2. 36900 3.063603e-01 #> 371 beta.1.,beta.2. 37000 3.043336e-01 #> 372 beta.1.,beta.2. 37100 3.023203e-01 #> 373 beta.1.,beta.2. 37200 3.003220e-01 #> 374 beta.1.,beta.2. 37300 2.983323e-01 #> 375 beta.1.,beta.2. 37400 2.963581e-01 #> 376 beta.1.,beta.2. 37500 2.944046e-01 #> 377 beta.1.,beta.2. 37600 2.924985e-01 #> 378 beta.1.,beta.2. 37700 2.906183e-01 #> 379 beta.1.,beta.2. 37800 2.887512e-01 #> 380 beta.1.,beta.2. 37900 2.869099e-01 #> 381 beta.1.,beta.2. 38000 2.850765e-01 #> 382 beta.1.,beta.2. 38100 2.832430e-01 #> 383 beta.1.,beta.2. 38200 2.814097e-01 #> 384 beta.1.,beta.2. 38300 2.796030e-01 #> 385 beta.1.,beta.2. 38400 2.778181e-01 #> 386 beta.1.,beta.2. 38500 2.760596e-01 #> 387 beta.1.,beta.2. 38600 2.743042e-01 #> 388 beta.1.,beta.2. 38700 2.725626e-01 #> 389 beta.1.,beta.2. 38800 2.708210e-01 #> 390 beta.1.,beta.2. 38900 2.690877e-01 #> 391 beta.1.,beta.2. 39000 2.673614e-01 #> 392 beta.1.,beta.2. 39100 2.656351e-01 #> 393 beta.1.,beta.2. 39200 2.639213e-01 #> 394 beta.1.,beta.2. 39300 2.622214e-01 #> 395 beta.1.,beta.2. 39400 2.605248e-01 #> 396 beta.1.,beta.2. 39500 2.588283e-01 #> 397 beta.1.,beta.2. 39600 2.571434e-01 #> 398 beta.1.,beta.2. 39700 2.554615e-01 #> 399 beta.1.,beta.2. 39800 2.537810e-01 #> 400 beta.1.,beta.2. 39900 2.521252e-01 #> 401 beta.1.,beta.2. 40000 2.504935e-01 #> 402 beta.1.,beta.2. 40100 2.488892e-01 #> 403 beta.1.,beta.2. 40200 2.472959e-01 #> 404 beta.1.,beta.2. 40300 2.457275e-01 #> 405 beta.1.,beta.2. 40400 2.441923e-01 #> 406 beta.1.,beta.2. 40500 2.426613e-01 #> 407 beta.1.,beta.2. 40600 2.411526e-01 #> 408 beta.1.,beta.2. 40700 2.396684e-01 #> 409 beta.1.,beta.2. 40800 2.382291e-01 #> 410 beta.1.,beta.2. 40900 2.367951e-01 #> 411 beta.1.,beta.2. 41000 2.353736e-01 #> 412 beta.1.,beta.2. 41100 2.339666e-01 #> 413 beta.1.,beta.2. 41200 2.325611e-01 #> 414 beta.1.,beta.2. 41300 2.311557e-01 #> 415 beta.1.,beta.2. 41400 2.297563e-01 #> 416 beta.1.,beta.2. 41500 2.283653e-01 #> 417 beta.1.,beta.2. 41600 2.269863e-01 #> 418 beta.1.,beta.2. 41700 2.256259e-01 #> 419 beta.1.,beta.2. 41800 2.242938e-01 #> 420 beta.1.,beta.2. 41900 2.229744e-01 #> 421 beta.1.,beta.2. 42000 2.216551e-01 #> 422 beta.1.,beta.2. 42100 2.203358e-01 #> 423 beta.1.,beta.2. 42200 2.190165e-01 #> 424 beta.1.,beta.2. 42300 2.176971e-01 #> 425 beta.1.,beta.2. 42400 2.163804e-01 #> 426 beta.1.,beta.2. 42500 2.150968e-01 #> 427 beta.1.,beta.2. 42600 2.138344e-01 #> 428 beta.1.,beta.2. 42700 2.125824e-01 #> 429 beta.1.,beta.2. 42800 2.113339e-01 #> 430 beta.1.,beta.2. 42900 2.100859e-01 #> 431 beta.1.,beta.2. 43000 2.088515e-01 #> 432 beta.1.,beta.2. 43100 2.076339e-01 #> 433 beta.1.,beta.2. 43200 2.064492e-01 #> 434 beta.1.,beta.2. 43300 2.052776e-01 #> 435 beta.1.,beta.2. 43400 2.041179e-01 #> 436 beta.1.,beta.2. 43500 2.029666e-01 #> 437 beta.1.,beta.2. 43600 2.018338e-01 #> 438 beta.1.,beta.2. 43700 2.007256e-01 #> 439 beta.1.,beta.2. 43800 1.996347e-01 #> 440 beta.1.,beta.2. 43900 1.985587e-01 #> 441 beta.1.,beta.2. 44000 1.974892e-01 #> 442 beta.1.,beta.2. 44100 1.964197e-01 #> 443 beta.1.,beta.2. 44200 1.953502e-01 #> 444 beta.1.,beta.2. 44300 1.942885e-01 #> 445 beta.1.,beta.2. 44400 1.932456e-01 #> 446 beta.1.,beta.2. 44500 1.922157e-01 #> 447 beta.1.,beta.2. 44600 1.911862e-01 #> 448 beta.1.,beta.2. 44700 1.901613e-01 #> 449 beta.1.,beta.2. 44800 1.891454e-01 #> 450 beta.1.,beta.2. 44900 1.881421e-01 #> 451 beta.1.,beta.2. 45000 1.871387e-01 #> 452 beta.1.,beta.2. 45100 1.861354e-01 #> 453 beta.1.,beta.2. 45200 1.851410e-01 #> 454 beta.1.,beta.2. 45300 1.841523e-01 #> 455 beta.1.,beta.2. 45400 1.831884e-01 #> 456 beta.1.,beta.2. 45500 1.822613e-01 #> 457 beta.1.,beta.2. 45600 1.813469e-01 #> 458 beta.1.,beta.2. 45700 1.804353e-01 #> 459 beta.1.,beta.2. 45800 1.795236e-01 #> 460 beta.1.,beta.2. 45900 1.786235e-01 #> 461 beta.1.,beta.2. 46000 1.777250e-01 #> 462 beta.1.,beta.2. 46100 1.768265e-01 #> 463 beta.1.,beta.2. 46200 1.759280e-01 #> 464 beta.1.,beta.2. 46300 1.750408e-01 #> 465 beta.1.,beta.2. 46400 1.741682e-01 #> 466 beta.1.,beta.2. 46500 1.733006e-01 #> 467 beta.1.,beta.2. 46600 1.724417e-01 #> 468 beta.1.,beta.2. 46700 1.715828e-01 #> 469 beta.1.,beta.2. 46800 1.707240e-01 #> 470 beta.1.,beta.2. 46900 1.698655e-01 #> 471 beta.1.,beta.2. 47000 1.690192e-01 #> 472 beta.1.,beta.2. 47100 1.681822e-01 #> 473 beta.1.,beta.2. 47200 1.673487e-01 #> 474 beta.1.,beta.2. 47300 1.665152e-01 #> 475 beta.1.,beta.2. 47400 1.656817e-01 #> 476 beta.1.,beta.2. 47500 1.648482e-01 #> 477 beta.1.,beta.2. 47600 1.640322e-01 #> 478 beta.1.,beta.2. 47700 1.632500e-01 #> 479 beta.1.,beta.2. 47800 1.624679e-01 #> 480 beta.1.,beta.2. 47900 1.616857e-01 #> 481 beta.1.,beta.2. 48000 1.609060e-01 #> 482 beta.1.,beta.2. 48100 1.601366e-01 #> 483 beta.1.,beta.2. 48200 1.593672e-01 #> 484 beta.1.,beta.2. 48300 1.585978e-01 #> 485 beta.1.,beta.2. 48400 1.578284e-01 #> 486 beta.1.,beta.2. 48500 1.570590e-01 #> 487 beta.1.,beta.2. 48600 1.562896e-01 #> 488 beta.1.,beta.2. 48700 1.555302e-01 #> 489 beta.1.,beta.2. 48800 1.547736e-01 #> 490 beta.1.,beta.2. 48900 1.540170e-01 #> 491 beta.1.,beta.2. 49000 1.532604e-01 #> 492 beta.1.,beta.2. 49100 1.525038e-01 #> 493 beta.1.,beta.2. 49200 1.517472e-01 #> 494 beta.1.,beta.2. 49300 1.509906e-01 #> 495 beta.1.,beta.2. 49400 1.502340e-01 #> 496 beta.1.,beta.2. 49500 1.494882e-01 #> 497 beta.1.,beta.2. 49600 1.487427e-01 #> 498 beta.1.,beta.2. 49700 1.479973e-01 #> 499 beta.1.,beta.2. 49800 1.472519e-01 #> 500 beta.1.,beta.2. 49900 1.465064e-01 #> 501 beta.1.,beta.2. 50000 1.457650e-01 #> #> attr(,\"class\") #> [1] \"evppi\" \"list\""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — evppi_plot_graph","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — evppi_plot_graph","text":"Base R ggplot2 versions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — evppi_plot_graph","text":"","code":"evppi_plot_base(evppi_obj, pos_legend, col = NULL, annot = FALSE) evppi_plot_ggplot(evppi_obj, pos_legend = c(0, 0.8), col = c(1, 1), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — evppi_plot_graph","text":"evppi_obj Object class evppi pos_legend Position legend col Colour annot Annotate EVPPI curve parameter names ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_qq_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Q-Q Plot — evppi_qq_plot","title":"Q-Q Plot — evppi_qq_plot","text":"Q-Q Plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_qq_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Q-Q Plot — evppi_qq_plot","text":"","code":"evppi_qq_plot(evppi, he, interv)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_residual_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual Plot — evppi_residual_plot","title":"Residual Plot — evppi_residual_plot","text":"Residual Plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_residual_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual Plot — evppi_residual_plot","text":"","code":"evppi_residual_plot(evppi, he, interv)"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Additive Model Fitting — fit.gam","title":"Gaussian Additive Model Fitting — fit.gam","text":"Gaussian Additive Model Fitting","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Additive Model Fitting — fit.gam","text":"","code":"fit.gam(parameter, inputs, x, form)"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Additive Model Fitting — fit.gam","text":"parameter Parameter inputs Inputs x Response variable form Formula","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian Additive Model Fitting — fit.gam","text":"List","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit Gaussian Process — fit.gp","title":"Fit Gaussian Process — fit.gp","text":"Fit Gaussian Process","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit Gaussian Process — fit.gp","text":"","code":"fit.gp(parameter, inputs, x, n.sim)"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit Gaussian Process — fit.gp","text":"parameter Parameters inputs Inputs x Response variable n.sim Number simulations","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit Gaussian Process — fit.gp","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit INLA — fit.inla","title":"Fit INLA — fit.inla","text":"Fit INLA","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit INLA — fit.inla","text":"","code":"fit.inla( parameter, inputs, x, mesh, data.scale, int.ord, convex.inner, convex.outer, cutoff, max.edge, h.value, family )"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit INLA — fit.inla","text":"parameter Parameters inputs Inputs x Response variable mesh Mesh data.scale data.scale int.ord int.ord convex.inner convex.inner convex.outer convex.outer cutoff Cut-max.edge Maximum edge h.value h.value family family","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit INLA — fit.inla","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"Credible interval ggplot geom — geom_cri","title":"Credible interval ggplot geom — geom_cri","text":"Credible interval ggplot geom","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Credible interval ggplot geom — geom_cri","text":"","code":"geom_cri(plot.cri = TRUE, params = NA)"},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_cri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Credible interval ggplot geom — geom_cri","text":"plot.cri plot CrI? Logical params Plot parameters including data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html","id":null,"dir":"Reference","previous_headings":"","what":"Geom Quadrant Text — geom_quad_txt","title":"Geom Quadrant Text — geom_quad_txt","text":"Geom Quadrant Text","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geom Quadrant Text — geom_quad_txt","text":"","code":"geom_quad_txt(he, graph_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geom Quadrant Text — geom_quad_txt","text":"bcea object containing results Bayesian modelling economic evaluation. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". plotting functions \"plotly\" implementation yet.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":null,"dir":"Reference","previous_headings":"","what":"GrassmannOptim — GrassmannOptim","title":"GrassmannOptim — GrassmannOptim","text":"function taken GrassmannOptim package Kofi Placid Adragni Seongho Wu https://cran.r-project.org/web/packages/GrassmannOptim/index.html","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GrassmannOptim — GrassmannOptim","text":"","code":"GrassmannOptim( objfun, W, sim_anneal = FALSE, temp_init = 20, cooling_rate = 2, max_iter_sa = 100, eps_conv = 1e-05, max_iter = 100, eps_grad = 1e-05, eps_f = .Machine$double.eps, verbose = FALSE )"},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GrassmannOptim — GrassmannOptim","text":"objfun objfun W W sim_anneal sim_anneal temp_init temp_init cooling_rate cooling_rate max_iter_sa max_iter_sa eps_conv eps_conv max_iter max_iter eps_grad eps_grad eps_f eps_f verbose verbose","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GrassmannOptim — GrassmannOptim","text":"List","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"Plots distribution Incremental Benefit (IB) given value willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"","code":"# S3 method for bcea ib.plot( he, comparison = NULL, wtp = 25000, bw = \"bcv\", n = 512, xlim = NULL, graph = c(\"base\", \"ggplot2\"), ... ) ib.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison case multiple interventions, specifies one used comparison reference. Default value NULL forces R consider first non-reference intervention comparator. Controls comparator used 2 interventions present wtp value willingness pay threshold. Default value 25000. bw Identifies smoothing bandwidth used construct kernel estimation IB density. n number equally spaced points density estimated. xlim limits plot x-axis. graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"ib ggplot object containing requested plot. Returned graph=\"ggplot2\". function produces plot distribution Incremental Benefit given value willingness pay parameter. dashed area indicates positive part distribution (.e. reference cost-effective comparator).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"IB plot base R version — ib_plot_base","title":"IB plot base R version — ib_plot_base","text":"Choice base R, ggplot2","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IB plot base R version — ib_plot_base","text":"","code":"ib_plot_base(he, comparison, wtp, bw, n, xlim) ib_plot_ggplot(he, comparison, wtp, bw, n, xlim)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ib_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"IB plot base R version — ib_plot_base","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Comparison intervention wtp Willingness pay bw band width n Number xlim x-axis limits","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Information-Rank Plot for bcea Class — info.rank.bcea","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"Produces plot similar tornado plot, based analysis EVPPI. parameter value willingness--pay threshold, barchart plotted describe ratio EVPPI (specific parameter) EVPI. represents relative `importance' parameter terms expected value information.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"","code":"# S3 method for bcea info.rank( he, inp, wtp = NULL, howManyPars = NA, graph = c(\"base\", \"ggplot2\", \"plotly\"), rel = TRUE, ... ) info.rank(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. inp Named list running createInputs() containing: parameter = vector parameters individual EVPPI calculated. can given string (vector strings) names numeric vector, corresponding column numbers important parameters. mat = matrix containing simulations parameters monitored call JAGS BUGS. matrix column names matching names parameters values vector parameter match least one values. wtp value wtp analysis performed. specified break-even point current model used. howManyPars Optional maximum number parameters included bar plot. Includes parameters default. graph string used select graphical engine use plotting. (partial-)match one two options \"base\" \"plotly\". Default value \"base\" rel Logical argument specifies whether ratio EVPPI EVPI (rel = TRUE, default) absolute value EVPPI used analysis. ... Additional options. include graphical parameters user can specify: xlim = limits x-axis; ca = font size axis label (default = 0.7 full size). cn = font size parameter names vector (default = 0.7 full size) - base graphics . mai = margins graph (default = c(1.36, 1.5, 1,1)) - base graphics .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"base graphics: data.frame containing ranking parameters value selected summary, chosen wtp; plotly: plotly object, incorporating $rank element data.frame . function produces 'Info-rank' plot. extension standard 'Tornado plots' presents ranking model parameters terms impact expected value information. parameter, specific individual EVPPI computed used measure impact uncertainty parameter decision-making process, terms large expected value gaining information .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"Anna Heath, Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"","code":"if (FALSE) { # Load the post-processed results of the MCMC simulation model # original JAGS output is can be downloaded from here # https://gianluca.statistica.it/book/bcea/code/vaccine.RData data(\"Vaccine\") m <- bcea(eff, cost) inp <- createInputs(vaccine_mat) info.rank(m, inp) info.rank(m, inp, graph = \"base\") info.rank(m, inp, graph = \"plotly\") info.rank(m, inp, graph = \"ggplot2\") }"},{"path":"https://n8thangreen.github.io/BCEA/reference/inforank_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Info Rank plot parameters — inforank_params","title":"Prepare Info Rank plot parameters — inforank_params","text":"Prepare Info Rank plot parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/inforank_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Info Rank plot parameters — inforank_params","text":"","code":"inforank_params(he, inp, wtp = NULL, rel, howManyPars, extra_args)"},{"path":"https://n8thangreen.github.io/BCEA/reference/inforank_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Info Rank plot parameters — inforank_params","text":"bcea object containing results Bayesian modelling economic evaluation. inp Inputs wtp Willingness pay rel Relative size howManyPars mnay parameters use? extra_args Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info_rank_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Info Rank Plot By Graph Device — info_rank_graph","title":"Info Rank Plot By Graph Device — info_rank_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info_rank_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Info Rank Plot By Graph Device — info_rank_graph","text":"","code":"info_rank_base(he, params) info_rank_ggplot(he, params) info_rank_plotly(params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/info_rank_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Info Rank Plot By Graph Device — info_rank_graph","text":"bcea object containing results Bayesian modelling economic evaluation. params Graph Parameters including data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Check bcea Class — is.bcea","title":"Check bcea Class — is.bcea","text":"Check bcea Class","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check bcea Class — is.bcea","text":"","code":"is.bcea(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check bcea Class — is.bcea","text":"bcea object containing results Bayesian modelling economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check bcea Class — is.bcea","text":".bcea returns TRUE FALSE depending whether argument bcea class object.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/kstar_vlines.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare K-star vertical lines — kstar_vlines","title":"Prepare K-star vertical lines — kstar_vlines","text":"Prepare K-star vertical lines","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/kstar_vlines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare K-star vertical lines — kstar_vlines","text":"","code":"kstar_vlines(he, plot_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/kstar_vlines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare K-star vertical lines — kstar_vlines","text":"bcea object containing results Bayesian modelling economic evaluation. plot_params Plots parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/line_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Labels for Plot — line_labels","title":"Create Labels for Plot — line_labels","text":"Create Labels Plot Swapped labels reference second","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/line_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Labels for Plot — line_labels","text":"","code":"line_labels(he, ...) # S3 method for default line_labels(he, ref_first = TRUE, ...) # S3 method for pairwise line_labels(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/line_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Labels for Plot — line_labels","text":"bcea object containing results Bayesian modelling economic evaluation. ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/loo_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Leave-one-out ranking — loo_rank","title":"Leave-one-out ranking — loo_rank","text":"Leave-one-ranking","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/loo_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leave-one-out ranking — loo_rank","text":"","code":"loo_rank(params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/loo_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leave-one-out ranking — loo_rank","text":"params Parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Mesh — make.mesh","title":"Make Mesh — make.mesh","text":"Fit using INLA methods.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Mesh — make.mesh","text":"","code":"make.mesh(data, convex.inner, convex.outer, cutoff, max.edge)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Mesh — make.mesh","text":"data Data convex.inner convex.inner convex.outer convex.outer cutoff Cut-value max.edge Maximum edge","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Mesh — make.mesh","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":null,"dir":"Reference","previous_headings":"","what":"INLA Fitting — make.proj","title":"INLA Fitting — make.proj","text":"INLA Fitting","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"INLA Fitting — make.proj","text":"","code":"make.proj(parameter, inputs, x, k, l)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"INLA Fitting — make.proj","text":"parameter Parameter inputs Inputs x Response variable k k l l","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"INLA Fitting — make.proj","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Report — make.report","title":"Make Report — make.report","text":"Constructs automated report output BCEA.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Report — make.report","text":"","code":"make.report(he, evppi = NULL, ext = \"pdf\", echo = FALSE, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Report — make.report","text":"bcea object containing results Bayesian modelling economic evaluation. evppi object obtained output call evppi (default NULL, essential producing report). ext string text indicate extension resulting output file. Possible options \"pdf\", \"docx\". requires use pandoc, knitr rmarkdown. echo string (default FALSE) instruct whether report also include BCEA commands used produce analyses. optional argument echo set TRUE (default = FALSE), commands also printed. ... Additional parameters. example, user can specify value willingness pay wtp, used resulting analyses (default break even point). Another additional parameter user can specify name file report written. can done simply passing optional argument filename=\"NAME\". user can also specify object including PSA simulations relevant model parameters. passed function (object psa_sims), make.report automatically construct \"Info-rank plot\", probabilistic form tornado plot, based Expected Value Partial Information. user can also specify optional argument show.tab (default=FALSE); set TRUE, table values Info-rank also shown.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Make Report — make.report","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Make Report — make.report","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make Report — make.report","text":"","code":"if (FALSE) { data(Vaccine, package = \"BCEA\") m <- bcea(eff, cost, ref = 2) make.report(m) }"},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":null,"dir":"Reference","previous_headings":"","what":"Legend Positioning — make_legend_plotly","title":"Legend Positioning — make_legend_plotly","text":"Legend Positioning","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legend Positioning — make_legend_plotly","text":"","code":"make_legend_plotly(pos_legend)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legend Positioning — make_legend_plotly","text":"pos_legend Position legend","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Legend Positioning — make_legend_plotly","text":"String","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots the probability that each intervention is the most cost-effective — mce.plot","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"function deprecated. Use ceac.plot instead. Plots probability n_int interventions analysed cost-effective.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"","code":"mce.plot(mce, pos = c(1, 0.5), graph = c(\"base\", \"ggplot2\"), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"mce output call function multi.ce. pos Parameter set position legend. Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, TRUE indicating use first standard FALSE use second one. Default value c(1,0.5), right inside plot area. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Optional arguments. example, possible specify colours used plot. done vector color=c(...). length vector colors needs number comparators included analysis, otherwise BCEA fall back default values (black, shades grey)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"mceplot ggplot object containing plot. Returned graph=\"ggplot2\".","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem if (FALSE) { # Load the processed results of the MCMC simulation model data(Vaccine) # # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) plot=FALSE # inhibits graphical output ) # mce <- multi.ce(m) # uses the results of the economic analysis # mce.plot(mce, # plots the probability of being most cost-effective graph=\"base\") # using base graphics # if(require(ggplot2)){ mce.plot(mce, # the same plot graph=\"ggplot2\") # using ggplot2 instead } }"},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"Runs cost-effectiveness analysis, accounts fact one intervention present market.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"","code":"mixedAn(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"bcea object containing results Bayesian modelling economic evaluation. value vector market shares associated interventions. size number possible comparators. default, assumes uniform distribution intervention.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"Creates object class mixedAn, subclass bcea contains results health economic evaluation mixed analysis case: Ubar array simulations ''known-distribution'' mixed utilities, value discrete grid approximation willingness pay parameter OL.star array simulations distribution Opportunity Loss mixed strategy, value discrete grid approximation willingness pay parameter evi.star Expected Value Information mixed strategy, value discrete grid approximation willingness pay parameter mkt.shares vector market shares associated available intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"Baio G, Russo P (2009). “decision-theoretic framework application cost-effectiveness analysis regulatory processes.” Pharmacoeconomics, 27(8), 5--16. ISSN 20356137, doi:10.1007/bf03320526 . Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) plot=FALSE) # inhibits graphical output mixedAn(m) <- NULL # uses the results of the mixed strategy # analysis (a \"mixedAn\" object) # the vector of market shares can be defined # externally. If NULL, then each of the T # interventions will have 1/T market share # produces the plots evi.plot(m)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"Computes plots probability n_int interventions analysed cost-effective cost-effectiveness acceptability frontier.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"","code":"# S3 method for bcea multi.ce(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"bcea object containing results Bayesian modelling economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"Original bcea object (list) class \"pairwise\" additional: p_best_interv matrix including probability intervention cost-effective values willingness pay parameter ceaf vector containing cost-effectiveness acceptability frontier","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) plot=FALSE # inhibits graphical output ) mce <- multi.ce(m) # uses the results of the economic analysis ceac.plot(mce) ceaf.plot(mce)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Multiple bcea Graphs — multiplot","title":"Plot Multiple bcea Graphs — multiplot","text":"Arrange plots grid. Sourced R graphics cookbook.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Multiple bcea Graphs — multiplot","text":"","code":"multiplot(plotlist = NULL, cols = 1, layout_config = NULL)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Multiple bcea Graphs — multiplot","text":"plotlist List ggplot objects cols Number columns layout_config Matrix plot configuration","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Multiple bcea Graphs — multiplot","text":"ggplot TableGrob object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for bcea — new_bcea","title":"Constructor for bcea — new_bcea","text":"Constructor bcea","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for bcea — new_bcea","text":"","code":"new_bcea(df_ce, k)"},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for bcea — new_bcea","text":"df_ce Dataframe simulation eff cost k Vector willingness pay values","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for bcea — new_bcea","text":"List object class bcea.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/num_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Get number of lines — num_lines","title":"Get number of lines — num_lines","text":"Get number lines","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/num_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get number of lines — num_lines","text":"","code":"num_lines(dat) # S3 method for pairwise num_lines(dat) # S3 method for bcea num_lines(dat) # S3 method for evppi num_lines(dat) # S3 method for default num_lines(dat)"},{"path":"https://n8thangreen.github.io/BCEA/reference/num_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get number of lines — num_lines","text":"dat Data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/openPDF.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatically open pdf output using default pdf viewer — openPDF","title":"Automatically open pdf output using default pdf viewer — openPDF","text":"Automatically open pdf output using default pdf viewer","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/openPDF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatically open pdf output using default pdf viewer — openPDF","text":"","code":"openPDF(file_name)"},{"path":"https://n8thangreen.github.io/BCEA/reference/openPDF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatically open pdf output using default pdf viewer — openPDF","text":"file_name String file names pdf","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Plot of the Health Economic Analysis — plot.bcea","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"Plots single graph Cost-Effectiveness plane, Expected Incremental Benefit, CEAC EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"","code":"# S3 method for bcea plot( x, comparison = NULL, wtp = 25000, pos = FALSE, graph = c(\"base\", \"ggplot2\"), ... )"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"x bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). wtp value willingness pay parameter. passed ceplane.plot. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods ceplane.plot eib.plot. Please see manual pages individual functions. Arguments like size, ICER.size plot.cri can supplied functions way. addition graph=\"ggplot2\" arguments named theme objects added plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"plot four graphical summaries health economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"default position legend cost-effectiveness plane (produced ceplane.plot) set c(1, 1.025) overriding default pos=FALSE, since multiple ggplot2 plots rendered slightly different way single plots.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA he <- bcea( e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) plot=FALSE # does not produce graphical outputs ) # Plots the summary plots for the \"bcea\" object m using base graphics plot(he, graph = \"base\") # Plots the same summary plots using ggplot2 if(require(ggplot2)){ plot(he, graph = \"ggplot2\") ##### Example of a customized plot.bcea with ggplot2 plot(he, graph = \"ggplot2\", # use ggplot2 theme = theme(plot.title=element_text(size=rel(1.25))), # theme elements must have a name ICER_size = 1.5, # hidden option in ceplane.plot size = rel(2.5) # modifies the size of k = labels ) # in ceplane.plot and eib.plot }"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"Summary plot health economic analysis risk aversion included.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"","code":"# S3 method for CEriskav plot(x, pos = c(0, 1), graph = c(\"base\", \"ggplot2\"), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"x object class CEriskav, subclass bcea, containing results economic analysis performed accounting risk aversion parameter (obtained output function CEriskav). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods, graphical parameters (see par).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"list(eib,evi) two-elements named list ggplot objects containing requested plots. Returned graph=\"ggplot2\". function produces two plots risk aversion analysis. first one EIB function discrete grid approximation willingness parameter possible values risk aversion parameter, r. second one similar plot EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"Plots Expected Incremental Benefit Expected Value Perfect Information risk aversion included utility function.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # # Load the processed results of the MCMC simulation model data(Vaccine) # # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) plot=FALSE # inhibits graphical output ) # # Define the vector of values for the risk aversion parameter, r, eg: r <- c(1e-10, 0.005, 0.020, 0.035) # # Run the cost-effectiveness analysis accounting for risk aversion # \\donttest{ CEriskav(m) <- r # } # # produce the plots # \\donttest{ plot(m) # } ## Alternative options, using ggplot2 # \\donttest{ plot(m, graph = \"ggplot2\") # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"Plot Expected Value Partial Information Respect Set Parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"","code":"# S3 method for evppi plot(x, pos = c(0, 0.8), graph = c(\"base\", \"ggplot2\"), col = c(1, 1), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"x object class evppi, obtained call function evppi. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". col Sets colour lines depicted graph. ... Arguments passed methods, graphical parameters (see par).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"Plot base R ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"","code":"if (FALSE) { data(Vaccine, package = \"BCEA\") treats <- c(\"Status quo\", \"Vaccination\") # Run the health economic evaluation using BCEA m <- bcea(e.pts, c.pts, ref = 2, interventions = treats) # Compute the EVPPI for a bunch of parameters inp <- createInputs(vaccine_mat) # Compute the EVPPI using INLA/SPDE if (require(\"INLA\")) { x0 <- evppi(m, c(\"beta.1.\" , \"beta.2.\"), input = inp$mat) plot(x0, pos = c(0,1)) x1 <- evppi(m, c(32,48,49), input = inp$mat) plot(x1, pos = \"topright\") plot(x0, col = c(\"black\", \"red\"), pos = \"topright\") plot(x0, col = c(2,3), pos = \"bottomright\") plot(x0, pos = c(0,1), graph = \"ggplot2\") plot(x1, pos = \"top\", graph = \"ggplot2\") plot(x0, col = c(\"black\", \"red\"), pos = \"right\", graph = \"ggplot2\") plot(x0, col = c(2,3), size = c(1,2), pos = \"bottom\", graph = \"ggplot2\") plot(x0, graph = \"ggplot2\", theme = ggplot2::theme_linedraw()) } if (FALSE) plot(x0, col = 3, pos = \"topright\") # The vector 'col' must have the number of elements for an EVPI # colour and each of the EVPPI parameters. Forced to black }"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"Mesh Plot — plot.mesh","title":"Mesh Plot — plot.mesh","text":"Option interactively saving plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mesh Plot — plot.mesh","text":"","code":"# S3 method for mesh plot(mesh, data, plot)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mesh Plot — plot.mesh","text":"mesh Mesh data Data plot Create plot? logical","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_eib_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Credible Intervals — plot_eib_cri","title":"Plot Credible Intervals — plot_eib_cri","text":"Bayesian posterior credible intervals willingness pay.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_eib_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Credible Intervals — plot_eib_cri","text":"","code":"plot_eib_cri(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_eib_cri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Credible Intervals — plot_eib_cri","text":"bcea object containing results Bayesian modelling economic evaluation. params Graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"Mesh Plot — plot_mesh","title":"Mesh Plot — plot_mesh","text":"Option interactively saving plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mesh Plot — plot_mesh","text":"","code":"plot_mesh(mesh, data, plot, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mesh Plot — plot_mesh","text":"mesh Mesh data Data plot Create plot? logical ... Additional parameters","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/post.density.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Process Fitting — post.density","title":"Gaussian Process Fitting — post.density","text":"Gaussian Process Fitting","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/post.density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Process Fitting — post.density","text":"","code":"post.density(hyperparams, parameter, x, input.matrix)"},{"path":"https://n8thangreen.github.io/BCEA/reference/post.density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Process Fitting — post.density","text":"hyperparams Hyperparameters parameter Parameters x Response variable input.matrix Input data matrix","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/prep.x.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Delta arrays — prep.x","title":"Prepare Delta arrays — prep.x","text":"Prepare Delta arrays","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep.x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Delta arrays — prep.x","text":"","code":"prep.x(he, seq_rows, k, l)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep.x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Delta arrays — prep.x","text":"bcea object containing results Bayesian modelling economic evaluation. seq_rows Rows (e,c) keep k e c? 1 2. l Columns (e,c) keep","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare output — prepare.output","title":"Prepare output — prepare.output","text":"Prepare output","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare output — prepare.output","text":"","code":"prepare.output(parameters, inputs)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare output — prepare.output","text":"parameters Parameters inputs Inputs","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare output — prepare.output","text":"name","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare CE-plane Parameters — prep_ceplane_params","title":"Prepare CE-plane Parameters — prep_ceplane_params","text":"ggplot format, combine user-supplied parameters defaults.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare CE-plane Parameters — prep_ceplane_params","text":"","code":"prep_ceplane_params(he, wtp, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare CE-plane Parameters — prep_ceplane_params","text":"bcea object containing results Bayesian modelling economic evaluation. wtp Willingness--pay ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare CE-plane Parameters — prep_ceplane_params","text":"List pf graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare EIB plot parameters — prep_eib_params","title":"Prepare EIB plot parameters — prep_eib_params","text":"Parameters general plotting devices.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare EIB plot parameters — prep_eib_params","text":"","code":"prep_eib_params(he, plot.cri, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare EIB plot parameters — prep_eib_params","text":"bcea object containing results Bayesian modelling economic evaluation. plot.cri Make title including credible interval? Logical ... Additional parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare EIB plot parameters — prep_eib_params","text":"List graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare frontier data — prep_frontier_data","title":"Prepare frontier data — prep_frontier_data","text":"Prepare frontier data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare frontier data — prep_frontier_data","text":"","code":"prep_frontier_data(he, threshold = NULL, start.origin = TRUE)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare frontier data — prep_frontier_data","text":"bcea object containing results Bayesian modelling economic evaluation. threshold Cost-effectiveness threshold .e angle line. Must >=0 NULL. start.origin frontier start ?","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare frontier data — prep_frontier_data","text":"List scatter.data, ceef.points, orig.avg","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"bcea Print Method — print.bcea","title":"bcea Print Method — print.bcea","text":"bcea Print Method","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bcea Print Method — print.bcea","text":"","code":"# S3 method for bcea print(x, digits = getOption(\"digits\"), give.attr = FALSE, no.list = TRUE, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"bcea Print Method — print.bcea","text":"x bcea object containing results Bayesian modelling economic evaluation. digits Minimal number significant digits, see print.default. give.attr Logical; TRUE (default), show attributes sub structures. .list Logical; TRUE, ‘list ...’ class printed. ... Potential arguments.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"bcea Print Method — print.bcea","text":"","code":"data(\"Vaccine\") he <- BCEA::bcea(eff, cost) #> No reference selected. Defaulting to first intervention."},{"path":"https://n8thangreen.github.io/BCEA/reference/quadrant_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Quadrant Parameters\r\nrequires just a single comparison group — quadrant_params","title":"Quadrant Parameters\r\nrequires just a single comparison group — quadrant_params","text":"Quadrant Parameters requires just single comparison group","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/quadrant_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quadrant Parameters\r\nrequires just a single comparison group — quadrant_params","text":"","code":"quadrant_params(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/quiet.html","id":null,"dir":"Reference","previous_headings":"","what":"Allow disabling of the cat messages — quiet","title":"Allow disabling of the cat messages — quiet","text":"Allow disabling cat messages","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/quiet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allow disabling of the cat messages — quiet","text":"","code":"quiet(x)"},{"path":"https://n8thangreen.github.io/BCEA/reference/quiet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allow disabling of the cat messages — quiet","text":"x Object quietly return","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Choose Graphical Engine — select_plot_type","title":"Choose Graphical Engine — select_plot_type","text":"base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Choose Graphical Engine — select_plot_type","text":"","code":"select_plot_type(graph)"},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Choose Graphical Engine — select_plot_type","text":"graph Type names; string","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Choose Graphical Engine — select_plot_type","text":"Plot ID integer 1:base R; 2:ggplot2; 3:plotly","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Comparisons Group — setComparisons","title":"Set Comparisons Group — setComparisons","text":"One alternative way set (e,c) comparison group. Simply recompute comparisons drop unwanted.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Comparisons Group — setComparisons","text":"","code":"setComparisons(he, comparison)"},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Comparisons Group — setComparisons","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2).","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Comparison Group — setComparisons_assign","title":"Set Comparison Group — setComparisons_assign","text":"One alternative way set (e,c) comparison group.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Comparison Group — setComparisons_assign","text":"","code":"setComparisons(he) <- value # S3 method for bcea setComparisons(he) <- value # S3 method for default setComparisons(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Comparison Group — setComparisons_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value Comparison","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Comparison Group — setComparisons_assign","text":"bcea-type object","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Maximum Willingness to Pay — setKmax_assign","title":"Set Maximum Willingness to Pay — setKmax_assign","text":"Alternative way define `K` statistic.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Maximum Willingness to Pay — setKmax_assign","text":"","code":"setKmax(he) <- value # S3 method for bcea setKmax(he) <- value # S3 method for default setKmax(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Maximum Willingness to Pay — setKmax_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value Maximum willingness pay","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Maximum Willingness to Pay — setKmax_assign","text":"bcea-type object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Reference Group — setReferenceGroup_assign","title":"Set Reference Group — setReferenceGroup_assign","text":"Alternative way define (e,c) reference group.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Reference Group — setReferenceGroup_assign","text":"","code":"setReferenceGroup(he) <- value # S3 method for bcea setReferenceGroup(he) <- value # S3 method for default setReferenceGroup(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Reference Group — setReferenceGroup_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value Reference group number","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Reference Group — setReferenceGroup_assign","text":"bcea-type object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Table of Simulation Statistics for the Health Economic Model — sim_table","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"Using input form MCMC simulations run health economic model, produces summary table simulations cost-effectiveness analysis.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"","code":"sim_table(he, ...) # S3 method for bcea sim_table(he, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"bcea object containing results Bayesian modelling economic evaluation. ... Additional arguments wtp value willingness pay threshold used summary table.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"Produces following elements: table table simulation statistics economic model names.cols vector labels associated column table wtp selected value willingness pay idx_wtp index associated selected value willingness pay threshold grid used run analysis","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff, # defines the variables of c=cost, # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000) # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) # Now can save the simulation exercise in an object using sim_table() sim_table(m, # uses the results of the economic evaluation wtp=25000) # selects the particular value for k #> $Table #> U1 U2 U* IB2_1 OL VI #> 1 -36.575816 -38.71760 -36.575816 -2.141786568 2.14178657 -1.75012057 #> 2 -27.925136 -27.67448 -27.674479 0.250657304 0.00000000 7.15121672 #> 3 -28.030244 -33.37394 -28.030244 -5.343696338 5.34369634 6.79545099 #> 4 -53.284080 -47.13734 -47.137342 6.146738369 0.00000000 -12.31164646 #> 5 -43.583889 -40.40469 -40.404691 3.179197609 0.00000000 -5.57899569 #> 6 -42.374564 -33.08547 -33.085465 9.289098731 0.00000000 1.74023011 #> 7 -32.442356 -37.50684 -32.442356 -5.064481574 5.06448157 2.38333915 #> 8 -51.938478 -44.82658 -44.826583 7.111894853 0.00000000 -10.00088760 #> 9 -24.636412 -23.98616 -23.986156 0.650256008 0.00000000 10.83953966 #> 10 -46.181241 -37.34967 -37.349674 8.831567129 0.00000000 -2.52397818 #> 11 -58.095859 -45.57886 -45.578863 12.516995944 0.00000000 -10.75316756 #> 12 -32.026167 -30.02358 -30.023579 2.002587855 0.00000000 4.80211644 #> 13 -24.890578 -26.54063 -24.890578 -1.650056731 1.65005673 9.93511782 #> 14 -51.083390 -34.23447 -34.234473 16.848916771 0.00000000 0.59122249 #> 15 -70.975370 -50.83694 -50.836939 20.138431719 0.00000000 -16.01124327 #> 16 -32.343576 -35.74223 -32.343576 -3.398653900 3.39865390 2.48211897 #> 17 -11.337758 -16.12295 -11.337758 -4.785191763 4.78519176 23.48793764 #> 18 -39.751995 -29.86185 -29.861855 9.890139925 0.00000000 4.96384036 #> 19 -22.209179 -20.04740 -20.047398 2.161781794 0.00000000 14.77829781 #> 20 -45.634345 -62.35286 -45.634345 -16.718519479 16.71851948 -10.80864921 #> 21 -57.546460 -58.46125 -57.546460 -0.914786605 0.91478660 -22.72076498 #> 22 -55.815147 -47.81379 -47.813791 8.001355368 0.00000000 -12.98809588 #> 23 -65.979134 -66.06333 -65.979134 -0.084192580 0.08419258 -31.15343861 #> 24 -15.835575 -26.56841 -15.835575 -10.732836457 10.73283646 18.99012037 #> 25 -21.058746 -35.30491 -21.058746 -14.246165005 14.24616500 13.76694955 #> 26 -60.520260 -42.14622 -42.146217 18.374042590 0.00000000 -7.32052195 #> 27 -28.205540 -35.51466 -28.205540 -7.309120295 7.30912030 6.62015527 #> 28 -32.546793 -35.80048 -32.546793 -3.253683844 3.25368384 2.27890260 #> 29 -16.380093 -21.60875 -16.380093 -5.228658088 5.22865809 18.44560236 #> 30 -13.457847 -14.79877 -13.457847 -1.340925281 1.34092528 21.36784849 #> 31 -65.583149 -52.36537 -52.365370 13.217778975 0.00000000 -17.53967468 #> 32 -17.028951 -19.81625 -17.028951 -2.787299396 2.78729940 17.79674468 #> 33 -26.907486 -24.45174 -24.451744 2.455741729 0.00000000 10.37395142 #> 34 -31.516522 -28.31237 -28.312370 3.204152450 0.00000000 6.51332535 #> 35 -30.040100 -30.98166 -30.040100 -0.941562516 0.94156252 4.78559531 #> 36 -41.829563 -34.43487 -34.434870 7.394693338 0.00000000 0.39082575 #> 37 -47.162775 -40.30695 -40.306946 6.855828925 0.00000000 -5.48125071 #> 38 -23.397917 -36.38016 -23.397917 -12.982237974 12.98223797 11.42777809 #> 39 -45.966264 -42.40697 -42.406965 3.559298695 0.00000000 -7.58127000 #> 40 -30.728312 -33.35439 -30.728312 -2.626079843 2.62607984 4.09738320 #> 41 -50.290582 -46.21712 -46.217119 4.073462811 0.00000000 -11.39142371 #> 42 -21.747744 -29.78883 -21.747744 -8.041085884 8.04108588 13.07795164 #> 43 -36.740928 -30.35715 -30.357146 6.383781601 0.00000000 4.46854936 #> 44 -24.905508 -39.28508 -24.905508 -14.379574532 14.37957453 9.92018707 #> 45 -53.629450 -39.94290 -39.942903 13.686546690 0.00000000 -5.11720771 #> 46 -32.731200 -28.45269 -28.452689 4.278510704 0.00000000 6.37300616 #> 47 -38.813444 -37.71039 -37.710393 1.103050871 0.00000000 -2.88469761 #> 48 -60.413052 -46.71309 -46.713085 13.699966967 0.00000000 -11.88738988 #> 49 -21.078197 -19.70921 -19.709211 1.368985396 0.00000000 15.11648388 #> 50 -31.283131 -32.32021 -31.283131 -1.037080196 1.03708020 3.54256394 #> 51 -32.450047 -41.29502 -32.450047 -8.844978195 8.84497820 2.37564867 #> 52 -53.773562 -50.06956 -50.069563 3.703998418 0.00000000 -15.24386791 #> 53 -39.256315 -31.63766 -31.637663 7.618651922 0.00000000 3.18803237 #> 54 -32.095946 -28.83281 -28.832811 3.263135708 0.00000000 5.99288467 #> 55 -41.251946 -44.26889 -41.251946 -3.016945048 3.01694505 -6.42625028 #> 56 -56.130175 -46.32287 -46.322871 9.807304205 0.00000000 -11.49717582 #> 57 -23.751435 -41.63394 -23.751435 -17.882509187 17.88250919 11.07426052 #> 58 -36.168239 -29.83677 -29.836770 6.331468540 0.00000000 4.98892489 #> 59 -31.311610 -31.06406 -31.064060 0.247550071 0.00000000 3.76163553 #> 60 -57.964741 -44.05080 -44.050797 13.913943492 0.00000000 -9.22510211 #> 61 -42.581161 -36.25501 -36.255012 6.326148865 0.00000000 -1.42931660 #> 62 -39.898754 -29.85403 -29.854033 10.044721225 0.00000000 4.97166211 #> 63 -53.781040 -44.48163 -44.481631 9.299409087 0.00000000 -9.65593570 #> 64 -69.614271 -48.36707 -48.367068 21.247202691 0.00000000 -13.54137270 #> 65 -16.468131 -25.18588 -16.468131 -8.717744390 8.71774439 18.35756408 #> 66 -19.604551 -26.60639 -19.604551 -7.001836291 7.00183629 15.22114415 #> 67 -45.405402 -43.17445 -43.174446 2.230955572 0.00000000 -8.34875062 #> 68 -26.864505 -27.54852 -26.864505 -0.684019271 0.68401927 7.96119020 #> 69 -37.607518 -36.44240 -36.442401 1.165116878 0.00000000 -1.61670555 #> 70 -38.487461 -41.94032 -38.487461 -3.452862170 3.45286217 -3.66176588 #> 71 -23.027365 -25.09422 -23.027365 -2.066858536 2.06685854 11.79833017 #> 72 -14.336558 -18.17653 -14.336558 -3.839968809 3.83996881 20.48913714 #> 73 -45.763256 -37.42407 -37.424070 8.339185994 0.00000000 -2.59837477 #> 74 -38.293169 -38.54554 -38.293169 -0.252373112 0.25237311 -3.46747371 #> 75 -50.172148 -47.85474 -47.854736 2.317412007 0.00000000 -13.02904027 #> 76 -23.809870 -22.01309 -22.013093 1.796777165 0.00000000 12.81260243 #> 77 -37.748501 -30.80923 -30.809229 6.939271892 0.00000000 4.01646583 #> 78 -23.524954 -27.07055 -23.524954 -3.545599930 3.54559993 11.30074119 #> 79 -47.140215 -32.83611 -32.836111 14.304104346 0.00000000 1.98958439 #> 80 -25.063105 -24.82709 -24.827085 0.236019793 0.00000000 9.99860992 #> 81 -37.676658 -35.71838 -35.718380 1.958278380 0.00000000 -0.89268464 #> 82 -27.961527 -37.20587 -27.961527 -9.244344164 9.24434416 6.86416789 #> 83 -26.488039 -32.34358 -26.488039 -5.855537156 5.85553716 8.33765678 #> 84 -17.818883 -28.87161 -17.818883 -11.052727716 11.05272772 17.00681255 #> 85 -39.009508 -35.86738 -35.867378 3.142130329 0.00000000 -1.04168258 #> 86 -38.533784 -31.32709 -31.327093 7.206691469 0.00000000 3.49860255 #> 87 -36.486330 -27.86485 -27.864848 8.621482106 0.00000000 6.96084695 #> 88 -59.789430 -57.11228 -57.112285 2.677145466 0.00000000 -22.28658919 #> 89 -24.151853 -26.45561 -24.151853 -2.303754714 2.30375471 10.67384233 #> 90 -16.370847 -19.69877 -16.370847 -3.327921974 3.32792197 18.45484816 #> 91 -29.632758 -29.51358 -29.513584 0.119174302 0.00000000 5.31211156 #> 92 -39.893569 -39.49942 -39.499421 0.394148010 0.00000000 -4.67372527 #> 93 -21.272298 -33.24018 -21.272298 -11.967880869 11.96788087 13.55339705 #> 94 -30.387725 -37.05681 -30.387725 -6.669085502 6.66908550 4.43797030 #> 95 -33.350816 -31.41187 -31.411871 1.938944615 0.00000000 3.41382385 #> 96 -40.029505 -35.15516 -35.155161 4.874343446 0.00000000 -0.32946605 #> 97 -17.702393 -26.73252 -17.702393 -9.030124646 9.03012465 17.12330254 #> 98 -16.127988 -24.72537 -16.127988 -8.597384356 8.59738436 18.69770758 #> 99 -50.581388 -43.91244 -43.912443 6.668944785 0.00000000 -9.08674742 #> 100 -64.221127 -53.02862 -53.028617 11.192510294 0.00000000 -18.20292172 #> 101 -36.130258 -35.79804 -35.798040 0.332217564 0.00000000 -0.97234507 #> 102 -35.362101 -34.25108 -34.251078 1.111022605 0.00000000 0.57461703 #> 103 -35.294245 -30.87344 -30.873443 4.420801341 0.00000000 3.95225210 #> 104 -57.272375 -38.28796 -38.287956 18.984419500 0.00000000 -3.46226020 #> 105 -32.479352 -25.94254 -25.942536 6.536816830 0.00000000 8.88315967 #> 106 -39.643964 -32.67332 -32.673319 6.970644816 0.00000000 2.15237657 #> 107 -31.195435 -30.59790 -30.597905 0.597530351 0.00000000 4.22779068 #> 108 -75.368749 -68.87114 -68.871141 6.497608011 0.00000000 -34.04544564 #> 109 -61.750837 -40.70930 -40.709297 21.041539633 0.00000000 -5.88360202 #> 110 -20.317452 -29.21184 -20.317452 -8.894387577 8.89438758 14.50824370 #> 111 -27.512964 -33.35347 -27.512964 -5.840509056 5.84050906 7.31273132 #> 112 -36.481115 -33.36829 -33.368290 3.112824813 0.00000000 1.45740490 #> 113 -32.820145 -38.43081 -32.820145 -5.610661383 5.61066138 2.00555042 #> 114 -36.545720 -41.35659 -36.545720 -4.810874470 4.81087447 -1.72002482 #> 115 -32.686484 -32.78388 -32.686484 -0.097400906 0.09740091 2.13921135 #> 116 -23.515710 -25.64790 -23.515710 -2.132185386 2.13218539 11.30998493 #> 117 -25.267368 -26.91231 -25.267368 -1.644946781 1.64494678 9.55832750 #> 118 -37.768277 -36.61777 -36.617767 1.150510161 0.00000000 -1.79207194 #> 119 -26.373342 -25.00879 -25.008792 1.364550375 0.00000000 9.81690373 #> 120 -29.410483 -27.68329 -27.683286 1.727196759 0.00000000 7.14240912 #> 121 -18.101748 -21.30367 -18.101748 -3.201923502 3.20192350 16.72394732 #> 122 -38.641283 -32.76287 -32.762875 5.878408065 0.00000000 2.06282036 #> 123 -33.765743 -44.97225 -33.765743 -11.206504323 11.20650432 1.05995245 #> 124 -23.770044 -32.16060 -23.770044 -8.390560525 8.39056052 11.05565098 #> 125 -30.701205 -40.14649 -30.701205 -9.445283451 9.44528345 4.12449063 #> 126 -50.582718 -46.21238 -46.212379 4.370339514 0.00000000 -11.38668364 #> 127 -20.976377 -22.33520 -20.976377 -1.358827239 1.35882724 13.84931785 #> 128 -22.305728 -22.21149 -22.211487 0.094241483 0.00000000 12.61420841 #> 129 -21.095577 -29.42030 -21.095577 -8.324721461 8.32472146 13.73011804 #> 130 -73.165434 -60.35362 -60.353618 12.811816080 0.00000000 -25.52792240 #> 131 -14.167808 -17.44756 -14.167808 -3.279747387 3.27974739 20.65788730 #> 132 -35.898002 -27.55970 -27.559703 8.338299594 0.00000000 7.26599255 #> 133 -40.270884 -39.93264 -39.932643 0.338240439 0.00000000 -5.10694782 #> 134 -38.734717 -41.17318 -38.734717 -2.438466452 2.43846645 -3.90902136 #> 135 -29.156116 -30.85489 -29.156116 -1.698769769 1.69876977 5.66957895 #> 136 -22.955637 -21.33305 -21.333052 1.622584124 0.00000000 13.49264284 #> 137 -62.252900 -42.70410 -42.704101 19.548799049 0.00000000 -7.87840533 #> 138 -102.177758 -98.97074 -98.970738 3.207020713 0.00000000 -64.14504225 #> 139 -32.796649 -32.64367 -32.643667 0.152981896 0.00000000 2.18202826 #> 140 -47.678110 -33.08435 -33.084353 14.593756554 0.00000000 1.74134216 #> 141 -21.151776 -25.22621 -21.151776 -4.074438158 4.07443816 13.67391883 #> 142 -32.122050 -30.08375 -30.083752 2.038298186 0.00000000 4.74194333 #> 143 -26.296902 -21.54334 -21.543335 4.753566466 0.00000000 13.28235983 #> 144 -43.025423 -36.88249 -36.882494 6.142929215 0.00000000 -2.05679820 #> 145 -42.776739 -36.51205 -36.512047 6.264691852 0.00000000 -1.68635164 #> 146 -37.569702 -36.68284 -36.682845 0.886856848 0.00000000 -1.85714960 #> 147 -24.209486 -27.40378 -24.209486 -3.194292535 3.19429253 10.61620946 #> 148 -47.982241 -48.38364 -47.982241 -0.401395621 0.40139562 -13.15654598 #> 149 -77.713228 -40.99638 -40.996379 36.716849222 0.00000000 -6.17068381 #> 150 -43.769330 -42.28314 -42.283136 1.486194422 0.00000000 -7.45744060 #> 151 -34.694712 -34.31585 -34.315852 0.378859966 0.00000000 0.50984346 #> 152 -41.453318 -36.63997 -36.639973 4.813345061 0.00000000 -1.81427779 #> 153 -16.005288 -31.02543 -16.005288 -15.020139478 15.02013948 18.82040685 #> 154 -25.713444 -28.85638 -25.713444 -3.142937387 3.14293739 9.11225157 #> 155 -37.226397 -35.77600 -35.776001 1.450396149 0.00000000 -0.95030597 #> 156 -55.145835 -48.32931 -48.329315 6.816520307 0.00000000 -13.50361959 #> 157 -32.063611 -34.97150 -32.063611 -2.907888768 2.90788877 2.76208425 #> 158 -26.914447 -27.30613 -26.914447 -0.391679010 0.39167901 7.91124801 #> 159 -30.465354 -34.34856 -30.465354 -3.883208099 3.88320810 4.36034114 #> 160 -17.128446 -22.01007 -17.128446 -4.881623036 4.88162304 17.69724973 #> 161 -18.982632 -21.73387 -18.982632 -2.751241073 2.75124107 15.84306340 #> 162 -29.627395 -31.66658 -29.627395 -2.039187271 2.03918727 5.19829996 #> 163 -48.904341 -46.09079 -46.090787 2.813553833 0.00000000 -11.26509137 #> 164 -18.961366 -37.81434 -18.961366 -18.852976457 18.85297646 15.86432938 #> 165 -54.053516 -40.70611 -40.706107 13.347409462 0.00000000 -5.88041128 #> 166 -23.368485 -27.95576 -23.368485 -4.587274212 4.58727421 11.45721065 #> 167 -68.408452 -55.10636 -55.106361 13.302090755 0.00000000 -20.28066590 #> 168 -14.396777 -19.08608 -14.396777 -4.689303738 4.68930374 20.42891833 #> 169 -21.877446 -22.32783 -21.877446 -0.450385931 0.45038593 12.94824965 #> 170 -52.088647 -39.23704 -39.237038 12.851609740 0.00000000 -4.41134223 #> 171 -40.602559 -38.00148 -38.001479 2.601079055 0.00000000 -3.17578416 #> 172 -90.117895 -64.17205 -64.172054 25.945841723 0.00000000 -29.34635819 #> 173 -42.967861 -41.29128 -41.291278 1.676582647 0.00000000 -6.46558312 #> 174 -18.919770 -24.94893 -18.919770 -6.029161507 6.02916151 15.90592528 #> 175 -27.905601 -24.48260 -24.482599 3.423001841 0.00000000 10.34309617 #> 176 -21.678387 -28.27715 -21.678387 -6.598765388 6.59876539 13.14730854 #> 177 -50.563328 -41.19976 -41.199759 9.363569435 0.00000000 -6.37406371 #> 178 -41.250746 -31.43356 -31.433562 9.817184236 0.00000000 3.39213366 #> 179 -26.031496 -22.41968 -22.419679 3.611816799 0.00000000 12.40601593 #> 180 -38.494198 -29.46339 -29.463386 9.030811584 0.00000000 5.36230906 #> 181 -48.839379 -48.48224 -48.482242 0.357137333 0.00000000 -13.65654643 #> 182 -56.157495 -47.01324 -47.013237 9.144257614 0.00000000 -12.18754196 #> 183 -33.728531 -28.12095 -28.120955 5.607576517 0.00000000 6.70474063 #> 184 -39.189497 -27.14658 -27.146580 12.042916152 0.00000000 7.67911492 #> 185 -31.813690 -25.48912 -25.489124 6.324565778 0.00000000 9.33657110 #> 186 -31.362853 -28.05437 -28.054367 3.308485529 0.00000000 6.77132829 #> 187 -43.777680 -38.65037 -38.650366 5.127314321 0.00000000 -3.82467024 #> 188 -57.779115 -37.64703 -37.647026 20.132089505 0.00000000 -2.82133030 #> 189 -30.089107 -27.57487 -27.574865 2.514241541 0.00000000 7.25083024 #> 190 -39.009576 -36.26101 -36.261014 2.748562204 0.00000000 -1.43531850 #> 191 -61.747320 -57.25016 -57.250158 4.497161323 0.00000000 -22.42446314 #> 192 -18.906172 -18.93203 -18.906172 -0.025862055 0.02586206 15.91952330 #> 193 -56.192418 -54.60127 -54.601272 1.591146473 0.00000000 -19.77557663 #> 194 -19.913740 -21.94147 -19.913740 -2.027729431 2.02772943 14.91195549 #> 195 -58.460399 -46.11007 -46.110069 12.350329984 0.00000000 -11.28437403 #> 196 -34.404608 -38.74589 -34.404608 -4.341280845 4.34128084 0.42108742 #> 197 -23.395499 -27.07562 -23.395499 -3.680120680 3.68012068 11.43019646 #> 198 -34.999742 -27.90102 -27.901016 7.098726119 0.00000000 6.92467946 #> 199 -37.690703 -39.77269 -37.690703 -2.081983179 2.08198318 -2.86500719 #> 200 -33.017476 -31.09734 -31.097345 1.920130630 0.00000000 3.72835039 #> 201 -16.911293 -23.02700 -16.911293 -6.115703832 6.11570383 17.91440223 #> 202 -19.342889 -23.85679 -19.342889 -4.513896800 4.51389680 15.48280598 #> 203 -27.260617 -29.87794 -27.260617 -2.617319288 2.61731929 7.56507813 #> 204 -22.972979 -21.96643 -21.966434 1.006544777 0.00000000 12.85926137 #> 205 -41.258759 -34.07300 -34.073004 7.185755468 0.00000000 0.75269171 #> 206 -30.176787 -36.90721 -30.176787 -6.730420547 6.73042055 4.64890881 #> 207 -31.819854 -36.28683 -31.819854 -4.466981046 4.46698105 3.00584182 #> 208 -15.929191 -22.93669 -15.929191 -7.007494219 7.00749422 18.89650396 #> 209 -38.234151 -38.41219 -38.234151 -0.178038501 0.17803850 -3.40845615 #> 210 -62.016744 -61.40633 -61.406325 0.610418345 0.00000000 -26.58062991 #> 211 -30.920267 -37.56909 -30.920267 -6.648825085 6.64882509 3.90542868 #> 212 -25.915982 -24.11569 -24.115694 1.800287779 0.00000000 10.71000132 #> 213 -23.821314 -33.97603 -23.821314 -10.154715820 10.15471582 11.00438179 #> 214 -13.685559 -17.78091 -13.685559 -4.095351514 4.09535151 21.14013606 #> 215 -61.026306 -55.22339 -55.223392 5.802913499 0.00000000 -20.39769707 #> 216 -27.915241 -35.54518 -27.915241 -7.629944317 7.62994432 6.91045482 #> 217 -27.912598 -24.57577 -24.575766 3.336831905 0.00000000 10.24992953 #> 218 -29.361519 -25.46925 -25.469246 3.892273301 0.00000000 9.35644952 #> 219 -12.601455 -22.99872 -12.601455 -10.397261736 10.39726174 22.22424077 #> 220 -16.385197 -24.37039 -16.385197 -7.985191325 7.98519132 18.44049820 #> 221 -16.073785 -18.56594 -16.073785 -2.492153481 2.49215348 18.75190986 #> 222 -10.511486 -15.03723 -10.511486 -4.525748435 4.52574844 24.31420906 #> 223 -36.407683 -27.70955 -27.709553 8.698129977 0.00000000 7.11614220 #> 224 -41.151571 -35.44398 -35.443981 5.707590081 0.00000000 -0.61828606 #> 225 -29.700500 -28.73529 -28.735287 0.965212388 0.00000000 6.09040790 #> 226 -19.742931 -29.96756 -19.742931 -10.224624550 10.22462455 15.08276386 #> 227 -46.338858 -49.28764 -46.338858 -2.948786348 2.94878635 -11.51316245 #> 228 -32.776804 -29.36455 -29.364546 3.412258791 0.00000000 5.46114964 #> 229 -45.075520 -42.97215 -42.972147 2.103373621 0.00000000 -8.14645149 #> 230 -34.046472 -33.24967 -33.249667 0.796805211 0.00000000 1.57602816 #> 231 -34.146439 -24.05113 -24.051131 10.095307203 0.00000000 10.77456399 #> 232 -43.595901 -42.34848 -42.348483 1.247417781 0.00000000 -7.52278809 #> 233 -35.920060 -35.71861 -35.718614 0.201446577 0.00000000 -0.89291839 #> 234 -47.450643 -47.17662 -47.176621 0.274022030 0.00000000 -12.35092529 #> 235 -50.188006 -51.37649 -50.188006 -1.188482307 1.18848231 -15.36231072 #> 236 -23.931307 -26.88720 -23.931307 -2.955896243 2.95589624 10.89438826 #> 237 -52.131349 -42.23503 -42.235033 9.896315871 0.00000000 -7.40933801 #> 238 -56.067014 -54.62011 -54.620112 1.446901481 0.00000000 -19.79441696 #> 239 -34.180301 -31.95235 -31.952347 2.227953995 0.00000000 2.87334799 #> 240 -25.680040 -26.74639 -25.680040 -1.066353927 1.06635393 9.14565488 #> 241 -51.606925 -42.52557 -42.525569 9.081355645 0.00000000 -7.69987417 #> 242 -61.374696 -51.85838 -51.858384 9.516311985 0.00000000 -17.03268867 #> 243 -39.649164 -39.61088 -39.610881 0.038283277 0.00000000 -4.78518541 #> 244 -36.829876 -32.59939 -32.599386 4.230490439 0.00000000 2.22630929 #> 245 -19.435898 -25.15988 -19.435898 -5.723984879 5.72398488 15.38979749 #> 246 -39.286065 -35.83978 -35.839778 3.446287062 0.00000000 -1.01408268 #> 247 -47.771150 -34.11548 -34.115483 13.655666492 0.00000000 0.71021227 #> 248 -65.590027 -56.88299 -56.882988 8.707038476 0.00000000 -22.05729271 #> 249 -30.152605 -21.74218 -21.742181 8.410423951 0.00000000 13.08351424 #> 250 -35.675897 -43.89898 -35.675897 -8.223085552 8.22308555 -0.85020199 #> 251 -36.380570 -39.16491 -36.380570 -2.784339887 2.78433989 -1.55487424 #> 252 -54.790452 -61.76705 -54.790452 -6.976597383 6.97659738 -19.96475647 #> 253 -43.974843 -40.20818 -40.208177 3.766665475 0.00000000 -5.38248200 #> 254 -28.054282 -29.91975 -28.054282 -1.865465062 1.86546506 6.77141372 #> 255 -41.701393 -37.42450 -37.424497 4.276895637 0.00000000 -2.59880216 #> 256 -42.688927 -34.12267 -34.122671 8.566255956 0.00000000 0.70302429 #> 257 -50.069982 -34.39023 -34.390228 15.679753865 0.00000000 0.43546707 #> 258 -12.441513 -16.35756 -12.441513 -3.916049188 3.91604919 22.38418275 #> 259 -36.842453 -27.35969 -27.359686 9.482767193 0.00000000 7.46600928 #> 260 -41.597584 -47.92649 -41.597584 -6.328903327 6.32890333 -6.77188870 #> 261 -20.124542 -25.20399 -20.124542 -5.079444699 5.07944470 14.70115348 #> 262 -20.024566 -25.57924 -20.024566 -5.554677596 5.55467760 14.80112898 #> 263 -41.074834 -44.20127 -41.074834 -3.126438694 3.12643869 -6.24913866 #> 264 -32.008027 -30.98090 -30.980899 1.027128054 0.00000000 3.84479635 #> 265 -74.782290 -51.80566 -51.805658 22.976631768 0.00000000 -16.97996260 #> 266 -29.614411 -29.25661 -29.256607 0.357803879 0.00000000 5.56908790 #> 267 -18.239860 -24.96996 -18.239860 -6.730099974 6.73009997 16.58583539 #> 268 -39.447020 -37.86202 -37.862022 1.584997404 0.00000000 -3.03632712 #> 269 -32.222699 -33.34726 -32.222699 -1.124558449 1.12455845 2.60299646 #> 270 -49.926383 -65.12687 -49.926383 -15.200488058 15.20048806 -15.10068795 #> 271 -20.049289 -23.54031 -20.049289 -3.491016558 3.49101656 14.77640605 #> 272 -21.018344 -28.16049 -21.018344 -7.142147881 7.14214788 13.80735176 #> 273 -23.063988 -24.53408 -23.063988 -1.470093400 1.47009340 11.76170692 #> 274 -45.332469 -36.85232 -36.852319 8.480149641 0.00000000 -2.02662382 #> 275 -22.501973 -25.81458 -22.501973 -3.312611612 3.31261161 12.32372271 #> 276 -17.259137 -21.34312 -17.259137 -4.083985427 4.08398543 17.56655831 #> 277 -64.258591 -53.43504 -53.435038 10.823552492 0.00000000 -18.60934300 #> 278 -31.883318 -33.88484 -31.883318 -2.001526607 2.00152661 2.94237747 #> 279 -40.748800 -43.27253 -40.748800 -2.523732025 2.52373202 -5.92310437 #> 280 -25.009024 -35.51786 -25.009024 -10.508837099 10.50883710 9.81667094 #> 281 -22.546125 -31.35496 -22.546125 -8.808830889 8.80883089 12.27957070 #> 282 -31.998409 -39.03063 -31.998409 -7.032219381 7.03221938 2.82728644 #> 283 -44.260399 -44.20954 -44.209535 0.050864295 0.00000000 -9.38383985 #> 284 -26.199287 -25.56317 -25.563171 0.636116743 0.00000000 9.26252477 #> 285 -59.461220 -58.10035 -58.100354 1.360865735 0.00000000 -23.27465887 #> 286 -14.259087 -17.21384 -14.259087 -2.954756122 2.95475612 20.56660792 #> 287 -33.132436 -32.33251 -32.332505 0.799930148 0.00000000 2.49318990 #> 288 -42.442961 -40.94860 -40.948604 1.494357113 0.00000000 -6.12290901 #> 289 -42.438076 -34.12373 -34.123731 8.314344832 0.00000000 0.70196443 #> 290 -46.859489 -52.73898 -46.859489 -5.879495688 5.87949569 -12.03379334 #> 291 -19.478622 -16.80279 -16.802794 2.675827412 0.00000000 18.02290103 #> 292 -18.614943 -27.32995 -18.614943 -8.715003836 8.71500384 16.21075223 #> 293 -20.389878 -24.82569 -20.389878 -4.435814335 4.43581434 14.43581704 #> 294 -29.967894 -24.74372 -24.743717 5.224177058 0.00000000 10.08197814 #> 295 -34.107373 -37.55141 -34.107373 -3.444035849 3.44403585 0.71832261 #> 296 -35.955811 -38.87673 -35.955811 -2.920915654 2.92091565 -1.13011518 #> 297 -34.015321 -29.74825 -29.748250 4.267070870 0.00000000 5.07744504 #> 298 -19.207198 -25.80459 -19.207198 -6.597394081 6.59739408 15.61849720 #> 299 -31.550028 -28.64972 -28.649724 2.900304696 0.00000000 6.17597181 #> 300 -41.950825 -35.79362 -35.793622 6.157202273 0.00000000 -0.96792716 #> 301 -52.712579 -40.65718 -40.657179 12.055400063 0.00000000 -5.83148406 #> 302 -20.811115 -23.16218 -20.811115 -2.351063402 2.35106340 14.01457994 #> 303 -28.638310 -28.22114 -28.221139 0.417171118 0.00000000 6.60455598 #> 304 -22.340743 -24.78799 -22.340743 -2.447245561 2.44724556 12.48495232 #> 305 -41.128228 -34.89209 -34.892092 6.236136116 0.00000000 -0.06639652 #> 306 -42.468376 -45.30761 -42.468376 -2.839235666 2.83923567 -7.64268018 #> 307 -22.202042 -26.91001 -22.202042 -4.707966491 4.70796649 12.62365333 #> 308 -26.107776 -27.16343 -26.107776 -1.055651991 1.05565199 8.71791941 #> 309 -32.469032 -44.21170 -32.469032 -11.742664401 11.74266440 2.35666293 #> 310 -36.250363 -27.93476 -27.934761 8.315602547 0.00000000 6.89093467 #> 311 -26.763658 -21.58826 -21.588260 5.175398431 0.00000000 13.23743556 #> 312 -24.996491 -26.19501 -24.996491 -1.198523091 1.19852309 9.82920469 #> 313 -27.690160 -25.57636 -25.576358 2.113801910 0.00000000 9.24933684 #> 314 -38.923543 -41.43007 -38.923543 -2.506523568 2.50652357 -4.09784762 #> 315 -28.741809 -31.57840 -28.741809 -2.836589206 2.83658921 6.08388590 #> 316 -29.244280 -32.08937 -29.244280 -2.845092102 2.84509210 5.58141565 #> 317 -19.496219 -23.40420 -19.496219 -3.907984137 3.90798414 15.32947677 #> 318 -22.919748 -24.63573 -22.919748 -1.715978985 1.71597898 11.90594771 #> 319 -28.403514 -39.82735 -28.403514 -11.423839350 11.42383935 6.42218151 #> 320 -43.053489 -43.17806 -43.053489 -0.124574167 0.12457417 -8.22779408 #> 321 -43.058924 -38.03332 -38.033318 5.025605587 0.00000000 -3.20762281 #> 322 -31.296193 -29.49555 -29.495549 1.800643580 0.00000000 5.33014640 #> 323 -57.015967 -56.05437 -56.054371 0.961595531 0.00000000 -21.22867606 #> 324 -50.463378 -40.83999 -40.839991 9.623386840 0.00000000 -6.01429566 #> 325 -32.891971 -27.40295 -27.402946 5.489024629 0.00000000 7.42274918 #> 326 -44.872848 -45.85009 -44.872848 -0.977243615 0.97724361 -10.04715306 #> 327 -31.603711 -37.08808 -31.603711 -5.484371642 5.48437164 3.22198460 #> 328 -50.686113 -37.78835 -37.788346 12.897766751 0.00000000 -2.96265048 #> 329 -70.509803 -59.14535 -59.145348 11.364455443 0.00000000 -24.31965248 #> 330 -31.584861 -27.75038 -27.750382 3.834479006 0.00000000 7.07531325 #> 331 -32.064159 -23.37741 -23.377405 8.686753892 0.00000000 11.44829023 #> 332 -43.120802 -35.82440 -35.824396 7.296406733 0.00000000 -0.99870040 #> 333 -24.313646 -31.25519 -24.313646 -6.941544782 6.94154478 10.51204920 #> 334 -32.396823 -29.20847 -29.208473 3.188349511 0.00000000 5.61722225 #> 335 -56.096359 -39.74006 -39.740064 16.356295123 0.00000000 -4.91436876 #> 336 -32.651208 -29.49728 -29.497283 3.153925334 0.00000000 5.32841272 #> 337 -24.059658 -25.91636 -24.059658 -1.856704225 1.85670423 10.76603722 #> 338 -33.530768 -30.65437 -30.654375 2.876392962 0.00000000 4.17132057 #> 339 -31.755923 -34.45332 -31.755923 -2.697401099 2.69740110 3.06977256 #> 340 -47.225676 -34.98134 -34.981336 12.244339439 0.00000000 -0.15564111 #> 341 -27.860413 -32.97466 -27.860413 -5.114249763 5.11424976 6.96528195 #> 342 -38.499073 -37.57375 -37.573754 0.925319176 0.00000000 -2.74805827 #> 343 -47.954119 -40.59726 -40.597258 7.356860262 0.00000000 -5.77156298 #> 344 -35.944491 -33.87800 -33.878001 2.066490141 0.00000000 0.94769468 #> 345 -33.596701 -33.91103 -33.596701 -0.314332735 0.31433273 1.22899402 #> 346 -22.280314 -26.56911 -22.280314 -4.288799504 4.28879950 12.54538156 #> 347 -39.555872 -41.54945 -39.555872 -1.993574945 1.99357495 -4.73017701 #> 348 -55.034614 -48.50193 -48.501935 6.532678848 0.00000000 -13.67623952 #> 349 -49.230797 -36.65947 -36.659466 12.571331161 0.00000000 -1.83377034 #> 350 -12.644273 -21.04204 -12.644273 -8.397770304 8.39777030 22.18142266 #> 351 -30.990696 -29.61684 -29.616842 1.373854235 0.00000000 5.20885308 #> 352 -38.940899 -39.97423 -38.940899 -1.033329411 1.03332941 -4.11520320 #> 353 -31.382038 -35.37906 -31.382038 -3.997017403 3.99701740 3.44365726 #> 354 -41.208441 -40.70267 -40.702675 0.505766361 0.00000000 -5.87697955 #> 355 -19.350505 -32.09608 -19.350505 -12.745570485 12.74557049 15.47519080 #> 356 -25.740305 -35.40820 -25.740305 -9.667899262 9.66789926 9.08539054 #> 357 -9.246346 -22.48854 -9.246346 -13.242193568 13.24219357 25.57934912 #> 358 -16.938204 -25.20375 -16.938204 -8.265544251 8.26554425 17.88749154 #> 359 -35.260784 -39.01318 -35.260784 -3.752396471 3.75239647 -0.43508874 #> 360 -62.746500 -57.59926 -57.599258 5.147242088 0.00000000 -22.77356263 #> 361 -49.778597 -33.67946 -33.679464 16.099133318 0.00000000 1.14623158 #> 362 -45.650314 -37.02326 -37.023262 8.627052008 0.00000000 -2.19756679 #> 363 -35.782719 -33.61093 -33.610927 2.171792259 0.00000000 1.21476828 #> 364 -32.287150 -29.66346 -29.663461 2.623688445 0.00000000 5.16223403 #> 365 -39.312632 -35.53679 -35.536787 3.775844792 0.00000000 -0.71109140 #> 366 -14.198326 -22.24242 -14.198326 -8.044090081 8.04409008 20.62736974 #> 367 -49.789702 -50.36333 -49.789702 -0.573627731 0.57362773 -14.96400622 #> 368 -81.765618 -58.65676 -58.656758 23.108860391 0.00000000 -23.83106243 #> 369 -55.325002 -55.97285 -55.325002 -0.647843726 0.64784373 -20.49930643 #> 370 -47.976465 -39.06683 -39.066827 8.909638469 0.00000000 -4.24113159 #> 371 -54.059432 -48.77017 -48.770172 5.289259595 0.00000000 -13.94447687 #> 372 -24.814477 -28.90409 -24.814477 -4.089611144 4.08961114 10.01121862 #> 373 -33.987011 -29.93844 -29.938443 4.048568401 0.00000000 4.88725247 #> 374 -31.932440 -27.82212 -27.822119 4.110320991 0.00000000 7.00357662 #> 375 -27.347021 -24.66487 -24.664867 2.682154158 0.00000000 10.16082877 #> 376 -50.360806 -25.64666 -25.646664 24.714142166 0.00000000 9.17903129 #> 377 -45.970278 -38.66360 -38.663601 7.306676348 0.00000000 -3.83790597 #> 378 -25.344551 -21.10959 -21.109585 4.234966186 0.00000000 13.71611002 #> 379 -28.540140 -25.40397 -25.403971 3.136169461 0.00000000 9.42172473 #> 380 -37.732509 -34.34299 -34.342989 3.389519784 0.00000000 0.48270653 #> 381 -29.206918 -37.81110 -29.206918 -8.604185443 8.60418544 5.61877702 #> 382 -23.418369 -25.85965 -23.418369 -2.441277695 2.44127770 11.40732651 #> 383 -55.028860 -47.11273 -47.112727 7.916133012 0.00000000 -12.28703158 #> 384 -23.246556 -21.37414 -21.374141 1.872415454 0.00000000 13.45155436 #> 385 -31.636229 -24.90962 -24.909618 6.726611124 0.00000000 9.91607769 #> 386 -50.167913 -29.78429 -29.784293 20.383619849 0.00000000 5.04140219 #> 387 -39.692559 -44.20035 -39.692559 -4.507787153 4.50778715 -4.86686338 #> 388 -22.346325 -30.05385 -22.346325 -7.707529047 7.70752905 12.47937068 #> 389 -16.676861 -24.41739 -16.676861 -7.740526097 7.74052610 18.14883472 #> 390 -42.717065 -43.16157 -42.717065 -0.444504585 0.44450459 -7.89136925 #> 391 -29.530537 -27.16250 -27.162499 2.368037859 0.00000000 7.66319639 #> 392 -79.031761 -57.22100 -57.220996 21.810765338 0.00000000 -22.39530035 #> 393 -19.685308 -18.21925 -18.219248 1.466059847 0.00000000 16.60644709 #> 394 -48.051896 -45.03667 -45.036672 3.015223736 0.00000000 -10.21097702 #> 395 -37.208094 -39.48890 -37.208094 -2.280803070 2.28080307 -2.38239862 #> 396 -21.430354 -26.14106 -21.430354 -4.710710084 4.71071008 13.39534092 #> 397 -25.892068 -25.14809 -25.148092 0.743976140 0.00000000 9.67760302 #> 398 -30.339236 -36.43538 -30.339236 -6.096139196 6.09613920 4.48645934 #> 399 -49.472745 -43.28537 -43.285370 6.187375057 0.00000000 -8.45967511 #> 400 -81.406187 -66.37244 -66.372443 15.033743883 0.00000000 -31.54674796 #> 401 -49.845547 -37.83498 -37.834984 12.010562979 0.00000000 -3.00928863 #> 402 -42.432483 -37.88792 -37.887921 4.544561884 0.00000000 -3.06222591 #> 403 -31.889823 -26.26984 -26.269839 5.619983608 0.00000000 8.55585627 #> 404 -36.572721 -32.39904 -32.399040 4.173681238 0.00000000 2.42665507 #> 405 -40.007454 -35.28086 -35.280859 4.726595642 0.00000000 -0.45516322 #> 406 -54.700371 -46.84058 -46.840582 7.859789199 0.00000000 -12.01488650 #> 407 -78.640668 -55.53126 -55.531261 23.109407132 0.00000000 -20.70556561 #> 408 -28.299021 -28.93950 -28.299021 -0.640483025 0.64048303 6.52667474 #> 409 -16.418955 -26.99987 -16.418955 -10.580915682 10.58091568 18.40674055 #> 410 -51.471282 -48.44679 -48.446791 3.024490666 0.00000000 -13.62109591 #> 411 -28.012699 -23.46549 -23.465491 4.547207532 0.00000000 11.36020422 #> 412 -16.616223 -18.02326 -16.616223 -1.407036011 1.40703601 18.20947269 #> 413 -29.901845 -33.98655 -29.901845 -4.084705725 4.08470572 4.92385010 #> 414 -55.402874 -38.26707 -38.267068 17.135806209 0.00000000 -3.44137225 #> 415 -57.093138 -43.13065 -43.130650 13.962488052 0.00000000 -8.30495492 #> 416 -17.373659 -20.58657 -17.373659 -3.212913826 3.21291383 17.45203646 #> 417 -27.521073 -47.86510 -27.521073 -20.344022608 20.34402261 7.30462258 #> 418 -49.127966 -46.01121 -46.011206 3.116760562 0.00000000 -11.18551046 #> 419 -52.097467 -43.33990 -43.339900 8.757566748 0.00000000 -8.51420482 #> 420 -27.902277 -25.21048 -25.210480 2.691796580 0.00000000 9.61521515 #> 421 -24.650320 -25.77015 -24.650320 -1.119827590 1.11982759 10.17537529 #> 422 -55.579027 -54.14658 -54.146575 1.432451835 0.00000000 -19.32087990 #> 423 -23.096051 -25.36241 -23.096051 -2.266360506 2.26636051 11.72964401 #> 424 -31.821541 -29.13669 -29.136692 2.684849374 0.00000000 5.68900342 #> 425 -25.450778 -26.74546 -25.450778 -1.294682921 1.29468292 9.37491726 #> 426 -25.613111 -35.20871 -25.613111 -9.595596818 9.59559682 9.21258406 #> 427 -41.511321 -39.71511 -39.715109 1.796211778 0.00000000 -4.88941393 #> 428 -33.201303 -29.11487 -29.114865 4.086438074 0.00000000 5.71083010 #> 429 -32.869316 -34.06579 -32.869316 -1.196477566 1.19647757 1.95637910 #> 430 -57.469810 -58.10366 -57.469810 -0.633854714 0.63385471 -22.64411479 #> 431 -26.058407 -27.81994 -26.058407 -1.761536995 1.76153699 8.76728819 #> 432 -40.502840 -34.95145 -34.951446 5.551394071 0.00000000 -0.12575042 #> 433 -31.553229 -26.06939 -26.069391 5.483838019 0.00000000 8.75630441 #> 434 -19.950067 -21.08426 -19.950067 -1.134196530 1.13419653 14.87562789 #> 435 -14.710720 -26.01829 -14.710720 -11.307565839 11.30756584 20.11497557 #> 436 -32.903108 -32.23274 -32.232740 0.670367887 0.00000000 2.59295535 #> 437 -20.954125 -22.47452 -20.954125 -1.520399179 1.52039918 13.87157009 #> 438 -11.149409 -16.39406 -11.149409 -5.244653723 5.24465372 23.67628672 #> 439 -17.252369 -20.04964 -17.252369 -2.797267692 2.79726769 17.57332641 #> 440 -62.760848 -46.73360 -46.733598 16.027250661 0.00000000 -11.90790239 #> 441 -21.800120 -30.13001 -21.800120 -8.329888857 8.32988886 13.02557577 #> 442 -95.389259 -76.24788 -76.247879 19.141379825 0.00000000 -41.42218380 #> 443 -55.712193 -52.20574 -52.205744 3.506448211 0.00000000 -17.38004905 #> 444 -55.178720 -45.73175 -45.731750 9.446970087 0.00000000 -10.90605468 #> 445 -32.839359 -28.89622 -28.896221 3.943137637 0.00000000 5.92947386 #> 446 -24.649549 -25.26327 -24.649549 -0.613723443 0.61372344 10.17614653 #> 447 -36.971162 -23.88754 -23.887538 13.083624109 0.00000000 10.93815724 #> 448 -29.037690 -28.67895 -28.678947 0.358742647 0.00000000 6.14674814 #> 449 -40.832560 -27.71767 -27.717672 13.114887551 0.00000000 7.10802291 #> 450 -29.636855 -25.87993 -25.879925 3.756929283 0.00000000 8.94576984 #> 451 -36.305597 -44.01786 -36.305597 -7.712263059 7.71226306 -1.47990135 #> 452 -30.596616 -24.14990 -24.149899 6.446717220 0.00000000 10.67579625 #> 453 -19.958204 -19.83291 -19.832907 0.125297098 0.00000000 14.99278807 #> 454 -26.404944 -24.17396 -24.173964 2.230980087 0.00000000 10.65173122 #> 455 -52.539123 -39.97742 -39.977419 12.561704666 0.00000000 -5.15172326 #> 456 -12.726405 -14.70937 -12.726405 -1.982960923 1.98296092 22.09928988 #> 457 -41.966986 -36.12576 -36.125760 5.841225917 0.00000000 -1.30006435 #> 458 -49.221621 -43.93022 -43.930220 5.291400380 0.00000000 -9.10452504 #> 459 -51.010860 -41.85978 -41.859779 9.151080634 0.00000000 -7.03408355 #> 460 -18.244587 -25.82940 -18.244587 -7.584817069 7.58481707 16.58110850 #> 461 -34.542478 -31.56323 -31.563234 2.979243557 0.00000000 3.26246087 #> 462 -21.038178 -30.14352 -21.038178 -9.105341782 9.10534178 13.78751778 #> 463 -30.912730 -31.20278 -30.912730 -0.290050790 0.29005079 3.91296558 #> 464 -31.983339 -23.85948 -23.859477 8.123862392 0.00000000 10.96621865 #> 465 -35.953317 -37.97291 -35.953317 -2.019590528 2.01959053 -1.12762189 #> 466 -48.813443 -41.14937 -41.149374 7.664068800 0.00000000 -6.32367901 #> 467 -44.051664 -38.05154 -38.051544 6.000120553 0.00000000 -3.22584827 #> 468 -28.167921 -29.27722 -28.167921 -1.109302186 1.10930219 6.65777471 #> 469 -19.199365 -22.68428 -19.199365 -3.484912937 3.48491294 15.62633040 #> 470 -24.635656 -27.00883 -24.635656 -2.373173192 2.37317319 10.19003896 #> 471 -38.373566 -46.28222 -38.373566 -7.908654651 7.90865465 -3.54787049 #> 472 -53.326629 -36.00996 -36.009959 17.316669848 0.00000000 -1.18426370 #> 473 -23.741179 -31.99491 -23.741179 -8.253726252 8.25372625 11.08451643 #> 474 -36.829587 -38.02244 -36.829587 -1.192854218 1.19285422 -2.00389135 #> 475 -27.079921 -30.00773 -27.079921 -2.927805002 2.92780500 7.74577423 #> 476 -58.604785 -50.27755 -50.277550 8.327235040 0.00000000 -15.45185480 #> 477 -33.875914 -35.24758 -33.875914 -1.371664054 1.37166405 0.94978140 #> 478 -48.180093 -45.77968 -45.779680 2.400413477 0.00000000 -10.95398433 #> 479 -58.055256 -57.79063 -57.790631 0.264624996 0.00000000 -22.96493602 #> 480 -20.745155 -22.03950 -20.745155 -1.294341905 1.29434190 14.08054021 #> 481 -29.394351 -33.04709 -29.394351 -3.652734751 3.65273475 5.43134450 #> 482 -50.458030 -39.35587 -39.355874 11.102156756 0.00000000 -4.53017832 #> 483 -29.186569 -29.40917 -29.186569 -0.222597892 0.22259789 5.63912585 #> 484 -26.636414 -35.83730 -26.636414 -9.200887691 9.20088769 8.18928094 #> 485 -49.251887 -36.05800 -36.058001 13.193885882 0.00000000 -1.23230553 #> 486 -30.373973 -32.66693 -30.373973 -2.292957958 2.29295796 4.45172280 #> 487 -19.365675 -27.50661 -19.365675 -8.140930215 8.14093022 15.46002007 #> 488 -79.678864 -65.98274 -65.982739 13.696125048 0.00000000 -31.15704342 #> 489 -27.824948 -32.00825 -27.824948 -4.183297937 4.18329794 7.00074737 #> 490 -67.928752 -49.55983 -49.559834 18.368918604 0.00000000 -14.73413846 #> 491 -34.965478 -30.34624 -30.346244 4.619233456 0.00000000 4.47945084 #> 492 -59.071931 -31.72459 -31.724587 27.347344885 0.00000000 3.10110882 #> 493 -38.043872 -39.89268 -38.043872 -1.848808145 1.84880814 -3.21817653 #> 494 -41.815217 -51.60690 -41.815217 -9.791682772 9.79168277 -6.98952132 #> 495 -120.680862 -78.58210 -78.582101 42.098761734 0.00000000 -43.75640532 #> 496 -24.284295 -26.02300 -24.284295 -1.738704608 1.73870461 10.54140080 #> 497 -24.482993 -31.73269 -24.482993 -7.249693601 7.24969360 10.34270189 #> 498 -56.936619 -47.09342 -47.093419 9.843200012 0.00000000 -12.26772320 #> 499 -38.781073 -28.19426 -28.194261 10.586812386 0.00000000 6.63143442 #> 500 -43.044038 -48.45335 -43.044038 -5.409310461 5.40931046 -8.21834295 #> 501 -47.118025 -38.70137 -38.701368 8.416656399 0.00000000 -3.87567282 #> 502 -30.433807 -26.01824 -26.018236 4.415570891 0.00000000 8.80745959 #> 503 -39.637049 -36.39772 -36.397716 3.239332499 0.00000000 -1.57202109 #> 504 -23.398597 -24.03067 -23.398597 -0.632077148 0.63207715 11.42709806 #> 505 -31.950985 -36.18565 -31.950985 -4.234668662 4.23466866 2.87471040 #> 506 -22.899612 -25.75646 -22.899612 -2.856843470 2.85684347 11.92608294 #> 507 -23.184337 -27.45873 -23.184337 -4.274390473 4.27439047 11.64135790 #> 508 -41.264892 -27.34487 -27.344871 13.920021132 0.00000000 7.48082417 #> 509 -45.728247 -44.07674 -44.076736 1.651511089 0.00000000 -9.25104052 #> 510 -35.348664 -38.89359 -35.348664 -3.544929621 3.54492962 -0.52296864 #> 511 -53.422793 -33.20665 -33.206652 20.216140951 0.00000000 1.61904334 #> 512 -30.229810 -33.53853 -30.229810 -3.308720551 3.30872055 4.59588559 #> 513 -17.233300 -29.85435 -17.233300 -12.621052348 12.62105235 17.59239533 #> 514 -48.343930 -56.03856 -48.343930 -7.694627015 7.69462701 -13.51823466 #> 515 -11.692667 -26.45538 -11.692667 -14.762714168 14.76271417 23.13302788 #> 516 -35.266323 -30.39890 -30.398896 4.867427193 0.00000000 4.42679976 #> 517 -24.193989 -25.91659 -24.193989 -1.722598965 1.72259896 10.63170664 #> 518 -30.529550 -24.99442 -24.994425 5.535125631 0.00000000 9.83127047 #> 519 -32.097426 -28.27028 -28.270282 3.827143794 0.00000000 6.55541339 #> 520 -23.895784 -27.53167 -23.895784 -3.635890978 3.63589098 10.92991137 #> 521 -50.181270 -47.26890 -47.268895 2.912374830 0.00000000 -12.44319983 #> 522 -28.210152 -28.65683 -28.210152 -0.446682444 0.44668244 6.61554371 #> 523 -25.025716 -31.46432 -25.025716 -6.438601917 6.43860192 9.79997960 #> 524 -17.317966 -19.47380 -17.317966 -2.155830210 2.15583021 17.50772956 #> 525 -40.595936 -35.89790 -35.897898 4.698038545 0.00000000 -1.07220253 #> 526 -32.815013 -41.01822 -32.815013 -8.203203816 8.20320382 2.01068211 #> 527 -25.756220 -25.44908 -25.449085 0.307135174 0.00000000 9.37661044 #> 528 -32.843404 -35.77822 -32.843404 -2.934817445 2.93481744 1.98229142 #> 529 -29.495385 -28.35029 -28.350286 1.145098050 0.00000000 6.47540887 #> 530 -34.054072 -36.33171 -34.054072 -2.277641152 2.27764115 0.77162346 #> 531 -14.904893 -23.73310 -14.904893 -8.828210720 8.82821072 19.92080213 #> 532 -37.696372 -36.20627 -36.206269 1.490103016 0.00000000 -1.38057354 #> 533 -20.261330 -17.97789 -17.977894 2.283435258 0.00000000 16.84780103 #> 534 -36.123215 -34.71312 -34.713124 1.410091032 0.00000000 0.11257152 #> 535 -34.169734 -27.43623 -27.436230 6.733503642 0.00000000 7.38946542 #> 536 -29.920431 -24.72467 -24.724668 5.195762399 0.00000000 10.10102711 #> 537 -18.960910 -26.91727 -18.960910 -7.956363094 7.95636309 15.86478577 #> 538 -31.468259 -35.28228 -31.468259 -3.814020774 3.81402077 3.35743675 #> 539 -40.019154 -40.08706 -40.019154 -0.067909089 0.06790909 -5.19345893 #> 540 -20.420177 -20.73350 -20.420177 -0.313321811 0.31332181 14.40551872 #> 541 -38.425118 -33.81820 -33.818195 4.606923159 0.00000000 1.00750023 #> 542 -22.726644 -34.96936 -22.726644 -12.242711856 12.24271186 12.09905167 #> 543 -43.650991 -57.94800 -43.650991 -14.297004944 14.29700494 -8.82529531 #> 544 -33.524695 -25.90479 -25.904795 7.619900716 0.00000000 8.92090057 #> 545 -29.273676 -32.55336 -29.273676 -3.279682206 3.27968221 5.55201952 #> 546 -29.749748 -29.43461 -29.434609 0.315139614 0.00000000 5.39108654 #> 547 -42.449118 -35.57179 -35.571793 6.877324604 0.00000000 -0.74609796 #> 548 -29.374824 -33.96608 -29.374824 -4.591257048 4.59125705 5.45087179 #> 549 -62.199825 -49.38354 -49.383541 12.816283206 0.00000000 -14.55784612 #> 550 -25.731060 -29.90379 -25.731060 -4.172726886 4.17272689 9.09463502 #> 551 -32.200386 -29.07722 -29.077218 3.123167161 0.00000000 5.74847694 #> 552 -29.533295 -34.01888 -29.533295 -4.485583952 4.48558395 5.29240057 #> 553 -20.618117 -29.72542 -20.618117 -9.107298607 9.10729861 14.20757784 #> 554 -31.613999 -44.34537 -31.613999 -12.731371587 12.73137159 3.21169634 #> 555 -34.124607 -21.34105 -21.341046 12.783560443 0.00000000 13.48464887 #> 556 -41.083825 -37.24803 -37.248029 3.835795764 0.00000000 -2.42233394 #> 557 -33.369162 -27.33603 -27.336030 6.033132212 0.00000000 7.48966567 #> 558 -31.057345 -35.79678 -31.057345 -4.739433177 4.73943318 3.76834989 #> 559 -29.123738 -27.66119 -27.661192 1.462545826 0.00000000 7.16450347 #> 560 -14.947314 -22.38510 -14.947314 -7.437786460 7.43778646 19.87838174 #> 561 -33.283995 -31.43934 -31.439336 1.844659717 0.00000000 3.38635974 #> 562 -51.845794 -37.46412 -37.464121 14.381673305 0.00000000 -2.63842524 #> 563 -32.516771 -37.84996 -32.516771 -5.333192666 5.33319267 2.30892417 #> 564 -53.277806 -45.74146 -45.741455 7.536350624 0.00000000 -10.91575973 #> 565 -28.557342 -40.49259 -28.557342 -11.935250065 11.93525007 6.26835347 #> 566 -57.916324 -42.93395 -42.933955 14.982369209 0.00000000 -8.10825921 #> 567 -57.255882 -36.22085 -36.220855 21.035027268 0.00000000 -1.39515961 #> 568 -19.822973 -21.86792 -19.822973 -2.044944378 2.04494438 15.00272185 #> 569 -30.084329 -32.28934 -30.084329 -2.205015788 2.20501579 4.74136622 #> 570 -54.050152 -50.64909 -50.649087 3.401064691 0.00000000 -15.82339212 #> 571 -19.141970 -25.18434 -19.141970 -6.042370802 6.04237080 15.68372496 #> 572 -48.721133 -43.34580 -43.345804 5.375329569 0.00000000 -8.52010834 #> 573 -30.780764 -36.05100 -30.780764 -5.270234925 5.27023492 4.04493168 #> 574 -42.170589 -40.87077 -40.870765 1.299823515 0.00000000 -6.04506989 #> 575 -21.545939 -23.69349 -21.545939 -2.147546556 2.14754656 13.27975599 #> 576 -74.661176 -71.36924 -71.369245 3.291931286 0.00000000 -36.54354945 #> 577 -49.700792 -41.70621 -41.706210 7.994581502 0.00000000 -6.88051507 #> 578 -19.651713 -24.61340 -19.651713 -4.961687066 4.96168707 15.17398225 #> 579 -34.061458 -35.02832 -34.061458 -0.966860914 0.96686091 0.76423734 #> 580 -83.974274 -56.84799 -56.847989 27.126285008 0.00000000 -22.02229321 #> 581 -30.483444 -30.63742 -30.483444 -0.153977186 0.15397719 4.34225104 #> 582 -30.258658 -33.03482 -30.258658 -2.776160827 2.77616083 4.56703775 #> 583 -24.760829 -29.27274 -24.760829 -4.511908177 4.51190818 10.06486656 #> 584 -42.664267 -46.78795 -42.664267 -4.123681582 4.12368158 -7.83857145 #> 585 -28.346076 -34.07851 -28.346076 -5.732436302 5.73243630 6.47961965 #> 586 -32.765553 -22.52607 -22.526072 10.239480544 0.00000000 12.29962300 #> 587 -56.913061 -38.97007 -38.970065 17.942995789 0.00000000 -4.14436992 #> 588 -33.910981 -32.74086 -32.740857 1.170123335 0.00000000 2.08483804 #> 589 -44.907666 -41.21730 -41.217298 3.690367321 0.00000000 -6.39160312 #> 590 -49.050170 -50.49458 -49.050170 -1.444408955 1.44440895 -14.22447420 #> 591 -31.419126 -26.27038 -26.270379 5.148746933 0.00000000 8.55531590 #> 592 -16.566955 -20.09725 -16.566955 -3.530299423 3.53029942 18.25874010 #> 593 -20.743652 -27.11818 -20.743652 -6.374525598 6.37452560 14.08204372 #> 594 -35.965556 -32.75033 -32.750328 3.215228349 0.00000000 2.07536722 #> 595 -39.012811 -50.97758 -39.012811 -11.964772560 11.96477256 -4.18711558 #> 596 -31.774414 -33.06533 -31.774414 -1.290920507 1.29092051 3.05128158 #> 597 -36.623598 -26.41388 -26.413884 10.209713865 0.00000000 8.41181097 #> 598 -37.623354 -40.49778 -37.623354 -2.874420878 2.87442088 -2.79765911 #> 599 -40.803563 -32.01117 -32.011170 8.792393039 0.00000000 2.81452515 #> 600 -18.538313 -23.53675 -18.538313 -4.998437657 4.99843766 16.28738195 #> 601 -27.217819 -34.24666 -27.217819 -7.028842100 7.02884210 7.60787593 #> 602 -32.396334 -32.02075 -32.020750 0.375583738 0.00000000 2.80494544 #> 603 -28.812184 -31.26342 -28.812184 -2.451237807 2.45123781 6.01351161 #> 604 -21.641477 -25.46970 -21.641477 -3.828222002 3.82822200 13.18421832 #> 605 -24.335938 -22.86293 -22.862934 1.473004000 0.00000000 11.96276107 #> 606 -28.565620 -37.81985 -28.565620 -9.254229998 9.25423000 6.26007501 #> 607 -34.686362 -33.62957 -33.629570 1.056792247 0.00000000 1.19612514 #> 608 -37.593407 -29.24701 -29.247010 8.346396833 0.00000000 5.57868556 #> 609 -21.723745 -22.83200 -21.723745 -1.108257352 1.10825735 13.10195001 #> 610 -30.844217 -43.38884 -30.844217 -12.544621174 12.54462117 3.98147850 #> 611 -31.453692 -32.24849 -31.453692 -0.794799048 0.79479905 3.37200357 #> 612 -18.280651 -18.95907 -18.280651 -0.678418631 0.67841863 16.54504473 #> 613 -27.559451 -27.72912 -27.559451 -0.169667908 0.16966791 7.26624431 #> 614 -38.878418 -39.11318 -38.878418 -0.234761683 0.23476168 -4.05272316 #> 615 -45.026930 -35.36524 -35.365235 9.661694063 0.00000000 -0.53954011 #> 616 -31.674043 -29.74781 -29.747808 1.926234730 0.00000000 5.07788686 #> 617 -21.262957 -18.57031 -18.570306 2.692650886 0.00000000 16.25538922 #> 618 -16.240462 -24.70101 -16.240462 -8.460549020 8.46054902 18.58523317 #> 619 -36.988440 -38.17125 -36.988440 -1.182808117 1.18280812 -2.16274473 #> 620 -29.996054 -39.98557 -29.996054 -9.989520149 9.98952015 4.82964117 #> 621 -38.256518 -38.68127 -38.256518 -0.424750509 0.42475051 -3.43082266 #> 622 -45.171302 -44.43136 -44.431358 0.739944094 0.00000000 -9.60566262 #> 623 -17.295390 -19.43233 -17.295390 -2.136938190 2.13693819 17.53030539 #> 624 -22.786569 -33.13922 -22.786569 -10.352649238 10.35264924 12.03912585 #> 625 -26.297341 -22.37879 -22.378791 3.918549440 0.00000000 12.44690419 #> 626 -33.884220 -34.71640 -33.884220 -0.832182948 0.83218295 0.94147490 #> 627 -36.179015 -33.64639 -33.646388 2.532627027 0.00000000 1.17930747 #> 628 -55.569875 -48.86676 -48.866757 6.703117495 0.00000000 -14.04106213 #> 629 -23.678868 -24.23941 -23.678868 -0.560546383 0.56054638 11.14682708 #> 630 -103.000028 -82.64507 -82.645070 20.354957993 0.00000000 -47.81937423 #> 631 -27.673873 -30.00550 -27.673873 -2.331622525 2.33162253 7.15182198 #> 632 -38.244451 -36.31110 -36.311098 1.933352554 0.00000000 -1.48540305 #> 633 -24.206030 -33.00672 -24.206030 -8.800689391 8.80068939 10.61966537 #> 634 -26.502719 -27.36077 -26.502719 -0.858052667 0.85805267 8.32297645 #> 635 -27.612222 -24.78012 -24.780122 2.832100092 0.00000000 10.04557372 #> 636 -28.307704 -28.40625 -28.307704 -0.098543786 0.09854379 6.51799175 #> 637 -24.300246 -25.88779 -24.300246 -1.587540959 1.58754096 10.52544960 #> 638 -27.650521 -27.17288 -27.172876 0.477644926 0.00000000 7.65281945 #> 639 -23.034904 -32.84395 -23.034904 -9.809051081 9.80905108 11.79079171 #> 640 -31.007796 -36.02797 -31.007796 -5.020172748 5.02017275 3.81789981 #> 641 -36.923811 -32.64128 -32.641276 4.282535227 0.00000000 2.18441909 #> 642 -28.739272 -33.46871 -28.739272 -4.729435601 4.72943560 6.08642346 #> 643 -27.207530 -24.67129 -24.671295 2.536234927 0.00000000 10.15440053 #> 644 -46.155008 -48.42937 -46.155008 -2.274366155 2.27436616 -11.32931262 #> 645 -32.877177 -34.59436 -32.877177 -1.717187169 1.71718717 1.94851849 #> 646 -21.039416 -30.64949 -21.039416 -9.610071218 9.61007122 13.78627893 #> 647 -12.535232 -17.02552 -12.535232 -4.490291396 4.49029140 22.29046372 #> 648 -71.752316 -55.70931 -55.709307 16.043009436 0.00000000 -20.88361148 #> 649 -57.564649 -50.01446 -50.014464 7.550185449 0.00000000 -15.18876870 #> 650 -14.534147 -19.02088 -14.534147 -4.486732998 4.48673300 20.29154801 #> 651 -43.861010 -30.92012 -30.920120 12.940889257 0.00000000 3.90557490 #> 652 -100.581078 -68.63295 -68.632955 31.948122885 0.00000000 -33.80725961 #> 653 -46.932119 -32.47424 -32.474241 14.457878080 0.00000000 2.35145399 #> 654 -23.483257 -32.60558 -23.483257 -9.122321408 9.12232141 11.34243827 #> 655 -41.619349 -35.22786 -35.227860 6.391488589 0.00000000 -0.40216482 #> 656 -37.565839 -26.93428 -26.934279 10.631560084 0.00000000 7.89141657 #> 657 -36.978212 -26.44392 -26.443918 10.534294263 0.00000000 8.38177729 #> 658 -18.762839 -19.07872 -18.762839 -0.315882319 0.31588232 16.06285617 #> 659 -23.881180 -30.69811 -23.881180 -6.816931202 6.81693120 10.94451519 #> 660 -20.212419 -24.23479 -20.212419 -4.022370194 4.02237019 14.61327653 #> 661 -27.386252 -29.60026 -27.386252 -2.214011508 2.21401151 7.43944358 #> 662 -33.760830 -29.95261 -29.952607 3.808222942 0.00000000 4.87308810 #> 663 -25.424113 -25.84590 -25.424113 -0.421787206 0.42178721 9.40158240 #> 664 -25.917525 -20.65974 -20.659738 5.257786994 0.00000000 14.16595695 #> 665 -37.985305 -35.18610 -35.186096 2.799209004 0.00000000 -0.36040095 #> 666 -54.452142 -41.55452 -41.554517 12.897625241 0.00000000 -6.72882132 #> 667 -40.430195 -35.31724 -35.317237 5.112957935 0.00000000 -0.49154164 #> 668 -43.338399 -40.45948 -40.459481 2.878917871 0.00000000 -5.63378563 #> 669 -25.503570 -36.38378 -25.503570 -10.880205803 10.88020580 9.32212577 #> 670 -43.904426 -36.63928 -36.639281 7.265144596 0.00000000 -1.81358591 #> 671 -57.185793 -53.11213 -53.112133 4.073660377 0.00000000 -18.28643749 #> 672 -47.848734 -42.76211 -42.762109 5.086625315 0.00000000 -7.93641350 #> 673 -34.882318 -34.38561 -34.385609 0.496709613 0.00000000 0.44008646 #> 674 -29.147228 -29.66610 -29.147228 -0.518872150 0.51887215 5.67846778 #> 675 -48.482399 -38.37061 -38.370610 10.111788557 0.00000000 -3.54491498 #> 676 -16.981128 -20.64239 -16.981128 -3.661265346 3.66126535 17.84456724 #> 677 -36.127876 -26.46127 -26.461274 9.666602109 0.00000000 8.36442115 #> 678 -16.293340 -22.00544 -16.293340 -5.712095424 5.71209542 18.53235503 #> 679 -20.779916 -21.38226 -20.779916 -0.602342595 0.60234259 14.04577922 #> 680 -27.156718 -33.60660 -27.156718 -6.449882809 6.44988281 7.66897712 #> 681 -42.291883 -35.52098 -35.520982 6.770901353 0.00000000 -0.69528644 #> 682 -29.271517 -32.05807 -29.271517 -2.786554577 2.78655458 5.55417813 #> 683 -27.628463 -26.40190 -26.401900 1.226562461 0.00000000 8.42379511 #> 684 -22.226143 -23.06578 -22.226143 -0.839638579 0.83963858 12.59955211 #> 685 -54.185047 -35.93904 -35.939040 18.246007318 0.00000000 -1.11334448 #> 686 -28.152602 -26.69086 -26.690856 1.461745440 0.00000000 8.13483902 #> 687 -27.376054 -26.38231 -26.382306 0.993748465 0.00000000 8.44338982 #> 688 -24.931914 -26.86728 -24.931914 -1.935362703 1.93536270 9.89378157 #> 689 -54.719323 -46.91228 -46.912283 7.807040141 0.00000000 -12.08658745 #> 690 -19.800244 -25.10816 -19.800244 -5.307920373 5.30792037 15.02545097 #> 691 -14.804353 -17.44094 -14.804353 -2.636588352 2.63658835 20.02134198 #> 692 -29.794403 -33.23988 -29.794403 -3.445475370 3.44547537 5.03129277 #> 693 -35.599124 -35.62242 -35.599124 -0.023292316 0.02329232 -0.77342897 #> 694 -24.134607 -29.07750 -24.134607 -4.942895030 4.94289503 10.69108869 #> 695 -34.809486 -53.75713 -34.809486 -18.947641436 18.94764144 0.01620974 #> 696 -41.974096 -29.98304 -29.983039 11.991056841 0.00000000 4.84265633 #> 697 -53.755659 -42.30166 -42.301660 11.453999651 0.00000000 -7.47596448 #> 698 -29.691431 -35.15264 -29.691431 -5.461207457 5.46120746 5.13426402 #> 699 -48.493545 -36.62196 -36.621965 11.871580555 0.00000000 -1.79626930 #> 700 -17.584084 -20.56742 -17.584084 -2.983336704 2.98333670 17.24161154 #> 701 -17.518956 -23.63513 -17.518956 -6.116176247 6.11617625 17.30673940 #> 702 -26.261052 -28.89561 -26.261052 -2.634560389 2.63456039 8.56464313 #> 703 -41.187338 -48.70107 -41.187338 -7.513732812 7.51373281 -6.36164238 #> 704 -48.483854 -55.23514 -48.483854 -6.751284858 6.75128486 -13.65815887 #> 705 -32.240835 -26.00339 -26.003389 6.237445276 0.00000000 8.82230608 #> 706 -44.028389 -39.78822 -39.788225 4.240164306 0.00000000 -4.96252942 #> 707 -69.791359 -67.62816 -67.628157 2.163202160 0.00000000 -32.80246194 #> 708 -45.958216 -36.00022 -36.000224 9.957992246 0.00000000 -1.17452847 #> 709 -16.686028 -24.08880 -16.686028 -7.402771656 7.40277166 18.13966776 #> 710 -34.524775 -32.03828 -32.038283 2.486491689 0.00000000 2.78741203 #> 711 -33.074563 -31.98969 -31.989693 1.084869648 0.00000000 2.83600198 #> 712 -24.072121 -33.79518 -24.072121 -9.723059976 9.72305998 10.75357461 #> 713 -50.905861 -45.65743 -45.657432 5.248429227 0.00000000 -10.83173693 #> 714 -22.458418 -22.80009 -22.458418 -0.341667464 0.34166746 12.36727777 #> 715 -29.877399 -32.09471 -29.877399 -2.217311640 2.21731164 4.94829652 #> 716 -57.616640 -43.88616 -43.886160 13.730480109 0.00000000 -9.06046462 #> 717 -70.737329 -64.36822 -64.368219 6.369110676 0.00000000 -29.54252343 #> 718 -35.589352 -33.37425 -33.374247 2.215104127 0.00000000 1.45144790 #> 719 -44.101915 -33.58816 -33.588163 10.513752693 0.00000000 1.23753266 #> 720 -47.065828 -45.67680 -45.676804 1.389023826 0.00000000 -10.85110859 #> 721 -17.378111 -20.60717 -17.378111 -3.229064179 3.22906418 17.44758462 #> 722 -48.154047 -47.62427 -47.624274 0.529772627 0.00000000 -12.79857893 #> 723 -20.051748 -25.59710 -20.051748 -5.545352971 5.54535297 14.77394716 #> 724 -24.296848 -34.31322 -24.296848 -10.016367825 10.01636782 10.52884740 #> 725 -19.983276 -30.92242 -19.983276 -10.939148988 10.93914899 14.84241965 #> 726 -36.220091 -34.70039 -34.700393 1.519697989 0.00000000 0.12530189 #> 727 -35.139692 -38.20258 -35.139692 -3.062891970 3.06289197 -0.31399643 #> 728 -36.661787 -38.14670 -36.661787 -1.484916533 1.48491653 -1.83609190 #> 729 -35.460999 -41.62391 -35.460999 -6.162907593 6.16290759 -0.63530353 #> 730 -36.384223 -29.42664 -29.426638 6.957585217 0.00000000 5.39905749 #> 731 -55.255368 -66.24716 -55.255368 -10.991787221 10.99178722 -20.42967256 #> 732 -45.784339 -46.34354 -45.784339 -0.559205458 0.55920546 -10.95864395 #> 733 -55.467076 -41.48269 -41.482694 13.984382145 0.00000000 -6.65699843 #> 734 -37.840622 -26.24507 -26.245075 11.595547610 0.00000000 8.58062056 #> 735 -64.659836 -49.28398 -49.283979 15.375856400 0.00000000 -14.45828397 #> 736 -39.613551 -37.61779 -37.617789 1.995762069 0.00000000 -2.79209393 #> 737 -42.686475 -51.68149 -42.686475 -8.995017727 8.99501773 -7.86077972 #> 738 -41.977859 -41.84302 -41.843021 0.134838210 0.00000000 -7.01732583 #> 739 -39.777808 -41.03040 -39.777808 -1.252589406 1.25258941 -4.95211292 #> 740 -40.140699 -35.14091 -35.140910 4.999789465 0.00000000 -0.31521423 #> 741 -34.482068 -35.60986 -34.482068 -1.127794217 1.12779422 0.34362758 #> 742 -21.319758 -30.23062 -21.319758 -8.910857166 8.91085717 13.50593749 #> 743 -27.065400 -45.94810 -27.065400 -18.882704101 18.88270410 7.76029535 #> 744 -53.464070 -50.19521 -50.195211 3.268858234 0.00000000 -15.36951612 #> 745 -29.694765 -52.32428 -29.694765 -22.629518392 22.62951839 5.13092994 #> 746 -22.246166 -21.68257 -21.682570 0.563596276 0.00000000 13.14312517 #> 747 -30.606060 -43.34910 -30.606060 -12.743036247 12.74303625 4.21963563 #> 748 -31.643657 -52.38442 -31.643657 -20.740767803 20.74076780 3.18203855 #> 749 -29.768552 -29.36097 -29.360967 0.407584692 0.00000000 5.46472803 #> 750 -27.278161 -30.56588 -27.278161 -3.287718486 3.28771849 7.54753431 #> 751 -41.608154 -43.13469 -41.608154 -1.526532433 1.52653243 -6.78245896 #> 752 -23.877312 -28.61065 -23.877312 -4.733342413 4.73334241 10.94838341 #> 753 -38.314867 -37.97133 -37.971325 0.343541754 0.00000000 -3.14563015 #> 754 -22.302942 -25.37243 -22.302942 -3.069484780 3.06948478 12.52275283 #> 755 -21.061007 -25.69744 -21.061007 -4.636435243 4.63643524 13.76468853 #> 756 -60.362083 -43.48413 -43.484126 16.877956762 0.00000000 -8.65843107 #> 757 -19.005495 -32.59850 -19.005495 -13.593000732 13.59300073 15.82020060 #> 758 -45.601031 -50.15290 -45.601031 -4.551865603 4.55186560 -10.77533614 #> 759 -38.358107 -36.12408 -36.124075 2.234031789 0.00000000 -1.29837991 #> 760 -24.441087 -28.49494 -24.441087 -4.053857230 4.05385723 10.38460819 #> 761 -36.354903 -52.25131 -36.354903 -15.896404190 15.89640419 -1.52920786 #> 762 -69.971236 -50.91848 -50.918483 19.052753389 0.00000000 -16.09278718 #> 763 -38.165425 -22.57157 -22.571574 15.593850287 0.00000000 12.25412099 #> 764 -22.920200 -21.48503 -21.485030 1.435169768 0.00000000 13.34066491 #> 765 -44.052940 -41.28027 -41.280275 2.772665256 0.00000000 -6.45457922 #> 766 -36.630057 -34.30833 -34.308333 2.321723398 0.00000000 0.51736221 #> 767 -46.067538 -37.22689 -37.226892 8.840645753 0.00000000 -2.40119710 #> 768 -75.262071 -60.16425 -60.164250 15.097820462 0.00000000 -25.33855494 #> 769 -19.301502 -26.31245 -19.301502 -7.010950100 7.01095010 15.52419363 #> 770 -37.798463 -35.18268 -35.182678 2.615785316 0.00000000 -0.35698266 #> 771 -56.605978 -43.78634 -43.786337 12.819641510 0.00000000 -8.96064125 #> 772 -30.158137 -29.60119 -29.601194 0.556942745 0.00000000 5.22450147 #> 773 -70.581256 -57.22425 -57.224248 13.357008360 0.00000000 -22.39855225 #> 774 -83.328383 -63.49388 -63.493885 19.834498709 0.00000000 -28.66818923 #> 775 -27.383194 -24.83538 -24.835380 2.547813896 0.00000000 9.99031555 #> 776 -24.687533 -26.68595 -24.687533 -1.998413049 1.99841305 10.13816211 #> 777 -38.168919 -38.65385 -38.168919 -0.484930753 0.48493075 -3.34322371 #> 778 -23.361377 -24.63014 -23.361377 -1.268765722 1.26876572 11.46431800 #> 779 -36.144074 -40.11087 -36.144074 -3.966796929 3.96679693 -1.31837865 #> 780 -83.367959 -52.68565 -52.685647 30.682311674 0.00000000 -17.85995157 #> 781 -17.280820 -27.71350 -17.280820 -10.432678822 10.43267882 17.54487483 #> 782 -19.374350 -27.84479 -19.374350 -8.470438349 8.47043835 15.45134506 #> 783 -26.036520 -26.12037 -26.036520 -0.083853378 0.08385338 8.78917537 #> 784 -32.273956 -35.94733 -32.273956 -3.673369539 3.67336954 2.55173917 #> 785 -36.317834 -40.42136 -36.317834 -4.103523354 4.10352335 -1.49213892 #> 786 -66.972223 -63.85969 -63.859693 3.112530368 0.00000000 -29.03399750 #> 787 -33.496078 -33.55759 -33.496078 -0.061508774 0.06150877 1.32961689 #> 788 -14.726783 -23.49375 -14.726783 -8.766962093 8.76696209 20.09891197 #> 789 -18.314830 -27.87102 -18.314830 -9.556194688 9.55619469 16.51086504 #> 790 -11.859303 -24.05484 -11.859303 -12.195538672 12.19553867 22.96639242 #> 791 -38.302503 -38.12831 -38.128314 0.174188914 0.00000000 -3.30261837 #> 792 -16.518990 -23.74759 -16.518990 -7.228596022 7.22859602 18.30670565 #> 793 -49.990470 -36.03803 -36.038029 13.952440859 0.00000000 -1.21233369 #> 794 -35.058645 -42.18463 -35.058645 -7.125987807 7.12598781 -0.23294988 #> 795 -24.735892 -26.26556 -24.735892 -1.529672750 1.52967275 10.08980315 #> 796 -21.528604 -22.66836 -21.528604 -1.139751978 1.13975198 13.29709159 #> 797 -50.285771 -33.74901 -33.749010 16.536761000 0.00000000 1.07668502 #> 798 -27.240957 -30.82440 -27.240957 -3.583444467 3.58344447 7.58473852 #> 799 -35.039390 -39.07693 -35.039390 -4.037536872 4.03753687 -0.21369448 #> 800 -26.815881 -26.17295 -26.172952 0.642929622 0.00000000 8.65274350 #> 801 -30.018330 -38.08396 -30.018330 -8.065627475 8.06562747 4.80736495 #> 802 -32.817912 -31.90774 -31.907744 0.910167574 0.00000000 2.91795118 #> 803 -21.702739 -30.03743 -21.702739 -8.334694815 8.33469482 13.12295669 #> 804 -28.186748 -31.66415 -28.186748 -3.477398952 3.47739895 6.63894696 #> 805 -51.255412 -38.88664 -38.886635 12.368777151 0.00000000 -4.06093977 #> 806 -37.227154 -38.04851 -37.227154 -0.821352542 0.82135254 -2.40145868 #> 807 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18.564924096 0.00000000 -8.72454248 #> 821 -12.021851 -18.15487 -12.021851 -6.133018843 6.13301884 22.80384441 #> 822 -32.292306 -42.71255 -32.292306 -10.420244916 10.42024492 2.53338895 #> 823 -51.445604 -54.64531 -51.445604 -3.199708976 3.19970898 -16.61990889 #> 824 -27.138755 -26.94209 -26.942089 0.196666211 0.00000000 7.88360677 #> 825 -42.891233 -29.17545 -29.175451 13.715782184 0.00000000 5.65024460 #> 826 -26.652664 -33.80428 -26.652664 -7.151616652 7.15161665 8.17303116 #> 827 -58.106004 -33.70801 -33.708009 24.397995404 0.00000000 1.11768671 #> 828 -75.913976 -55.40180 -55.401802 20.512173741 0.00000000 -20.57610717 #> 829 -22.164701 -24.85288 -22.164701 -2.688176870 2.68817687 12.66099392 #> 830 -24.296165 -25.47363 -24.296165 -1.177462714 1.17746271 10.52953055 #> 831 -41.505855 -36.35584 -36.355844 5.150011296 0.00000000 -1.53014886 #> 832 -25.184077 -25.17551 -25.175506 0.008571006 0.00000000 9.65018921 #> 833 -34.960619 -31.77049 -31.770488 3.190130825 0.00000000 3.05520706 #> 834 -28.337895 -42.63458 -28.337895 -14.296685386 14.29668539 6.48780042 #> 835 -37.132266 -50.89076 -37.132266 -13.758492362 13.75849236 -2.30657094 #> 836 -46.812858 -39.05814 -39.058139 7.754718957 0.00000000 -4.23244363 #> 837 -25.360168 -21.67096 -21.670965 3.689203044 0.00000000 13.15473038 #> 838 -80.307314 -72.27332 -72.273320 8.033993579 0.00000000 -37.44762474 #> 839 -23.865747 -21.64710 -21.647097 2.218650298 0.00000000 13.17859863 #> 840 -28.692002 -24.52981 -24.529809 4.162193259 0.00000000 10.29588663 #> 841 -38.864104 -35.69677 -35.696774 3.167330129 0.00000000 -0.87107827 #> 842 -28.830840 -27.45771 -27.457715 1.373125695 0.00000000 7.36798069 #> 843 -52.338228 -38.43021 -38.430210 13.908017925 0.00000000 -3.60451506 #> 844 -38.194302 -42.21644 -38.194302 -4.022135571 4.02213557 -3.36860621 #> 845 -51.267761 -56.58512 -51.267761 -5.317357640 5.31735764 -16.44206553 #> 846 -27.425589 -26.91016 -26.910158 0.515430892 0.00000000 7.91553716 #> 847 -46.964820 -34.14595 -34.145950 12.818869799 0.00000000 0.67974487 #> 848 -69.948361 -58.21453 -58.214535 11.733826502 0.00000000 -23.38883926 #> 849 -22.565197 -31.35577 -22.565197 -8.790577480 8.79057748 12.26049838 #> 850 -52.752755 -46.71495 -46.714950 6.037805442 0.00000000 -11.88925455 #> 851 -46.358195 -41.23692 -41.236922 5.121273542 0.00000000 -6.41122656 #> 852 -35.892906 -39.78711 -35.892906 -3.894200965 3.89420096 -1.06721063 #> 853 -40.202667 -40.72536 -40.202667 -0.522689015 0.52268901 -5.37697145 #> 854 -41.625298 -43.61490 -41.625298 -1.989598279 1.98959828 -6.79960307 #> 855 -27.528954 -21.91579 -21.915789 5.613165354 0.00000000 12.90990675 #> 856 -55.571381 -46.35836 -46.358360 9.213021711 0.00000000 -11.53266437 #> 857 -34.402609 -34.30217 -34.302174 0.100434950 0.00000000 0.52352154 #> 858 -22.756093 -24.37069 -22.756093 -1.614595722 1.61459572 12.06960235 #> 859 -38.680828 -38.17853 -38.178528 0.502300410 0.00000000 -3.35283236 #> 860 -49.234869 -41.83267 -41.832666 7.402202945 0.00000000 -7.00697117 #> 861 -38.960149 -41.15109 -38.960149 -2.190941338 2.19094134 -4.13445397 #> 862 -63.857382 -54.10906 -54.109059 9.748323302 0.00000000 -19.28336341 #> 863 -31.177706 -29.93048 -29.930485 1.247220738 0.00000000 4.89521048 #> 864 -40.363029 -35.32875 -35.328749 5.034279633 0.00000000 -0.50305356 #> 865 -32.514886 -34.11995 -32.514886 -1.605066510 1.60506651 2.31080930 #> 866 -46.579931 -39.91749 -39.917493 6.662437860 0.00000000 -5.09179770 #> 867 -25.594053 -25.73878 -25.594053 -0.144731614 0.14473161 9.23164228 #> 868 -26.280421 -24.54068 -24.540677 1.739743976 0.00000000 10.28501862 #> 869 -31.059032 -25.87988 -25.879884 5.179147124 0.00000000 8.94581090 #> 870 -26.925822 -27.31467 -26.925822 -0.388847335 0.38884733 7.89987313 #> 871 -48.302524 -61.13593 -48.302524 -12.833404131 12.83340413 -13.47682876 #> 872 -52.343015 -39.08331 -39.083312 13.259703467 0.00000000 -4.25761632 #> 873 -28.070898 -28.10876 -28.070898 -0.037858308 0.03785831 6.75479710 #> 874 -30.596681 -35.01496 -30.596681 -4.418280693 4.41828069 4.22901480 #> 875 -17.113713 -19.86929 -17.113713 -2.755576082 2.75557608 17.71198194 #> 876 -30.988352 -25.60191 -25.601910 5.386442115 0.00000000 9.22378577 #> 877 -24.302616 -22.38162 -22.381616 1.921000034 0.00000000 12.44407907 #> 878 -36.927773 -22.15007 -22.150067 14.777705876 0.00000000 12.67562812 #> 879 -42.768976 -41.20309 -41.203093 1.565883026 0.00000000 -6.37739780 #> 880 -27.810045 -23.77593 -23.775933 4.034111950 0.00000000 11.04976206 #> 881 -27.035196 -27.53486 -27.035196 -0.499663718 0.49966372 7.79049899 #> 882 -26.633828 -23.72083 -23.720833 2.912995124 0.00000000 11.10486214 #> 883 -11.603238 -19.34979 -11.603238 -7.746551719 7.74655172 23.22245695 #> 884 -37.350648 -25.21385 -25.213846 12.136802115 0.00000000 9.61184955 #> 885 -35.764025 -51.28410 -35.764025 -15.520077634 15.52007763 -0.93832968 #> 886 -30.528418 -29.19541 -29.195413 1.333005028 0.00000000 5.63028225 #> 887 -19.664684 -33.00711 -19.664684 -13.342427453 13.34242745 15.16101105 #> 888 -26.945836 -30.73729 -26.945836 -3.791452506 3.79145251 7.87985913 #> 889 -31.795032 -32.25140 -31.795032 -0.456369018 0.45636902 3.03066355 #> 890 -19.864072 -25.46546 -19.864072 -5.601384196 5.60138420 14.96162379 #> 891 -43.449781 -34.84675 -34.846752 8.603028898 0.00000000 -0.02105685 #> 892 -48.431579 -36.92244 -36.922436 11.509142934 0.00000000 -2.09674061 #> 893 -58.545939 -51.24628 -51.246277 7.299662749 0.00000000 -16.42058125 #> 894 -44.473891 -37.53303 -37.533026 6.940864523 0.00000000 -2.70733077 #> 895 -27.464096 -25.18566 -25.185664 2.278432407 0.00000000 9.64003177 #> 896 -31.985743 -27.28566 -27.285655 4.700088091 0.00000000 7.54004019 #> 897 -28.629047 -28.67101 -28.629047 -0.041960933 0.04196093 6.19664824 #> 898 -24.022227 -22.64191 -22.641909 1.380317867 0.00000000 12.18378611 #> 899 -39.767679 -40.65619 -39.767679 -0.888515191 0.88851519 -4.94198417 #> 900 -27.058792 -29.70067 -27.058792 -2.641882610 2.64188261 7.76690335 #> 901 -50.714242 -35.89239 -35.892392 14.821849899 0.00000000 -1.06669708 #> 902 -41.618441 -27.90616 -27.906156 13.712284769 0.00000000 6.91953917 #> 903 -23.858741 -28.49932 -23.858741 -4.640583769 4.64058377 10.96695416 #> 904 -29.713827 -33.99794 -29.713827 -4.284116260 4.28411626 5.11186853 #> 905 -41.751568 -50.67227 -41.751568 -8.920703846 8.92070385 -6.92587272 #> 906 -41.354902 -35.59825 -35.598254 5.756648325 0.00000000 -0.77255869 #> 907 -22.441692 -24.59212 -22.441692 -2.150427818 2.15042782 12.38400289 #> 908 -23.711423 -30.97018 -23.711423 -7.258754464 7.25875446 11.11427272 #> 909 -22.952597 -27.77520 -22.952597 -4.822602851 4.82260285 11.87309822 #> 910 -34.623674 -29.30186 -29.301858 5.321815711 0.00000000 5.52383747 #> 911 -38.242953 -36.63384 -36.633844 1.609108360 0.00000000 -1.80814882 #> 912 -36.578542 -32.44066 -32.440657 4.137884817 0.00000000 2.38503810 #> 913 -19.409410 -28.23941 -19.409410 -8.830003423 8.83000342 15.41628559 #> 914 -30.291435 -32.47680 -30.291435 -2.185368378 2.18536838 4.53426021 #> 915 -41.488242 -34.55997 -34.559969 6.928273201 0.00000000 0.26572671 #> 916 -27.670235 -28.94721 -27.670235 -1.276976270 1.27697627 7.15545988 #> 917 -19.293019 -25.32584 -19.293019 -6.032821327 6.03282133 15.53267584 #> 918 -40.170858 -40.29967 -40.170858 -0.128816294 0.12881629 -5.34516256 #> 919 -30.619475 -32.53209 -30.619475 -1.912613364 1.91261336 4.20622021 #> 920 -32.831904 -33.44078 -32.831904 -0.608876050 0.60887605 1.99379145 #> 921 -46.948254 -33.40729 -33.407286 13.540968028 0.00000000 1.41840927 #> 922 -13.170228 -31.26175 -13.170228 -18.091521986 18.09152199 21.65546707 #> 923 -31.089490 -25.32508 -25.325084 5.764406748 0.00000000 9.50061179 #> 924 -29.535986 -40.37809 -29.535986 -10.842102776 10.84210278 5.28970973 #> 925 -46.770086 -56.78714 -46.770086 -10.017058723 10.01705872 -11.94439034 #> 926 -37.877645 -43.84422 -37.877645 -5.966577482 5.96657748 -3.05194927 #> 927 -21.609433 -28.17352 -21.609433 -6.564089933 6.56408993 13.21626195 #> 928 -29.350343 -22.34239 -22.342385 7.007957804 0.00000000 12.48331008 #> 929 -22.581503 -26.80641 -22.581503 -4.224904307 4.22490431 12.24419193 #> 930 -19.929154 -20.89432 -19.929154 -0.965165211 0.96516521 14.89654095 #> 931 -35.853191 -34.15562 -34.155619 1.697572041 0.00000000 0.67007669 #> 932 -29.280579 -35.66376 -29.280579 -6.383183587 6.38318359 5.54511583 #> 933 -42.782708 -37.12598 -37.125975 5.656732830 0.00000000 -2.30028011 #> 934 -64.398227 -52.68949 -52.689491 11.708735710 0.00000000 -17.86379610 #> 935 -54.375214 -43.86415 -43.864145 10.511068979 0.00000000 -9.03845001 #> 936 -31.105217 -38.24942 -31.105217 -7.144200109 7.14420011 3.72047868 #> 937 -57.280995 -45.58849 -45.588489 11.692505324 0.00000000 -10.76279393 #> 938 -20.314048 -30.83581 -20.314048 -10.521760053 10.52176005 14.51164695 #> 939 -47.717675 -40.38705 -40.387050 7.330624702 0.00000000 -5.56135490 #> 940 -35.017103 -31.77475 -31.774748 3.242355139 0.00000000 3.05094754 #> 941 -28.972912 -37.92054 -28.972912 -8.947624724 8.94762472 5.85278370 #> 942 -61.709263 -41.83632 -41.836323 19.872940260 0.00000000 -7.01062734 #> 943 -28.498268 -26.42468 -26.424679 2.073589096 0.00000000 8.40101680 #> 944 -23.476430 -32.77582 -23.476430 -9.299391160 9.29939116 11.34926577 #> 945 -24.839448 -33.38054 -24.839448 -8.541094674 8.54109467 9.98624705 #> 946 -71.360195 -53.39980 -53.399797 17.960397900 0.00000000 -18.57410164 #> 947 -31.257565 -30.87163 -30.871630 0.385935137 0.00000000 3.95406509 #> 948 -46.666844 -35.06900 -35.069004 11.597839166 0.00000000 -0.24330904 #> 949 -33.773768 -35.18319 -33.773768 -1.409423485 1.40942349 1.05192776 #> 950 -49.664049 -41.50245 -41.502447 8.161601836 0.00000000 -6.67675168 #> 951 -25.020204 -24.96256 -24.962563 0.057640900 0.00000000 9.86313265 #> 952 -50.784434 -35.57488 -35.574881 15.209553481 0.00000000 -0.74918558 #> 953 -47.133325 -44.79773 -44.797729 2.335595875 0.00000000 -9.97203366 #> 954 -15.195139 -18.65556 -15.195139 -3.460424906 3.46042491 19.63055652 #> 955 -58.959383 -50.64467 -50.644665 8.314717988 0.00000000 -15.81896982 #> 956 -37.131031 -39.17107 -37.131031 -2.040035839 2.04003584 -2.30533537 #> 957 -24.064513 -22.93854 -22.938544 1.125969273 0.00000000 11.88715117 #> 958 -15.834385 -23.42056 -15.834385 -7.586177171 7.58617717 18.99130994 #> 959 -30.307759 -29.21873 -29.218734 1.089024986 0.00000000 5.60696113 #> 960 -31.958113 -29.84855 -29.848549 2.109564410 0.00000000 4.97714682 #> 961 -28.570159 -36.18767 -28.570159 -7.617513223 7.61751322 6.25553619 #> 962 -42.529496 -37.83789 -37.837891 4.691604961 0.00000000 -3.01219544 #> 963 -51.352502 -41.09164 -41.091641 10.260860948 0.00000000 -6.26594538 #> 964 -35.772453 -26.09408 -26.094077 9.678376352 0.00000000 8.73161873 #> 965 -21.182119 -24.31967 -21.182119 -3.137546837 3.13754684 13.64357621 #> 966 -41.536898 -36.11154 -36.111537 5.425361506 0.00000000 -1.28584137 #> 967 -45.802594 -38.16879 -38.168789 7.633805431 0.00000000 -3.34309351 #> 968 -31.060313 -28.26441 -28.264414 2.795898975 0.00000000 6.56128170 #> 969 -31.543288 -28.71105 -28.711052 2.832236136 0.00000000 6.11464341 #> 970 -47.005711 -47.06168 -47.005711 -0.055971604 0.05597160 -12.18001586 #> 971 -43.348800 -50.27717 -43.348800 -6.928372094 6.92837209 -8.52310477 #> 972 -18.095025 -21.23151 -18.095025 -3.136485082 3.13648508 16.73067066 #> 973 -37.093331 -36.21202 -36.212021 0.881310055 0.00000000 -1.38632569 #> 974 -20.436049 -22.96945 -20.436049 -2.533403030 2.53340303 14.38964663 #> 975 -51.468477 -38.33349 -38.333485 13.134992288 0.00000000 -3.50778971 #> 976 -29.045457 -30.49799 -29.045457 -1.452534256 1.45253426 5.78023794 #> 977 -28.464792 -40.71263 -28.464792 -12.247837324 12.24783732 6.36090300 #> 978 -40.064048 -31.25327 -31.253271 8.810776482 0.00000000 3.57242398 #> 979 -23.098403 -26.82179 -23.098403 -3.723391350 3.72339135 11.72729237 #> 980 -24.156203 -24.90490 -24.156203 -0.748701646 0.74870165 10.66949266 #> 981 -18.176479 -22.35662 -18.176479 -4.180139211 4.18013921 16.64921596 #> 982 -27.210469 -31.21309 -27.210469 -4.002620995 4.00262100 7.61522651 #> 983 -56.970749 -54.47260 -54.472596 2.498153118 0.00000000 -19.64690070 #> 984 -32.078136 -35.10138 -32.078136 -3.023245302 3.02324530 2.74755926 #> 985 -42.039296 -36.08732 -36.087318 5.951977416 0.00000000 -1.26162316 #> 986 -38.579652 -36.05915 -36.059150 2.520502260 0.00000000 -1.23345433 #> 987 -24.653947 -35.08838 -24.653947 -10.434429882 10.43442988 10.17174875 #> 988 -30.105686 -38.72078 -30.105686 -8.615091202 8.61509120 4.72000927 #> 989 -55.837030 -44.00264 -44.002638 11.834392744 0.00000000 -9.17694241 #> 990 -58.380768 -51.32742 -51.327420 7.053348118 0.00000000 -16.50172487 #> 991 -36.079700 -29.23238 -29.232384 6.847316399 0.00000000 5.59331151 #> 992 -30.576011 -28.39595 -28.395946 2.180064907 0.00000000 6.42974933 #> 993 -16.767220 -23.58544 -16.767220 -6.818216019 6.81821602 18.05847571 #> 994 -89.942217 -60.93182 -60.931819 29.010397336 0.00000000 -26.10612395 #> 995 -46.337516 -47.47181 -46.337516 -1.134293175 1.13429317 -11.51182060 #> 996 -63.170017 -49.02473 -49.024726 14.145290909 0.00000000 -14.19903034 #> 997 -23.653379 -44.83142 -23.653379 -21.178036470 21.17803647 11.17231593 #> 998 -21.476949 -31.85681 -21.476949 -10.379865068 10.37986507 13.34874585 #> 999 -26.663848 -24.42500 -24.425002 2.238845425 0.00000000 10.40069284 #> 1000 -35.234486 -32.59782 -32.597825 2.636661457 0.00000000 2.22787079 #> Average -36.054123 -34.82570 -32.411228 1.228428062 2.41446695 2.41446695 #> #> $names.cols #> [1] \"U1\" \"U2\" \"U*\" \"IB2_1\" \"OL\" \"VI\" #> #> $wtp #> [1] 25000 #> #> $ind.table #> [1] 251 #>"},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":null,"dir":"Reference","previous_headings":"","what":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","text":"data set contains results Bayesian analysis used model clinical output costs associated health economic evaluation four different smoking cessation interventions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","text":"data list including variables needed smoking cessation cost-effectiveness analysis. variables follows: list(\"cost\") matrix 500 simulations posterior distribution overall costs associated four strategies list(\"data\") dataset containing characteristics smokers UK population list(\"eff\") matrix 500 simulations posterior distribution clinical benefits associated four strategies list(\"life.years\") matrix 500 simulations posterior distribution life years gained strategy list(\"pi_post\") matrix 500 simulations posterior distribution event smoking cessation strategy list(\"smoking\") data frame containing inputs needed network meta-analysis model. data.frame object contains: nobs: record ID number, s: study ID number, : intervention ID number, r_i: number patients quit smoking, n_i: total number patients row-specific arm b_i: reference intervention study list(\"smoking_mat\") matrix obtained running network meta-analysis model based data contained smoking object list(\"treats\") vector labels associated four strategies","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","text":"Effectiveness data adapted Hasselblad V. (1998). Meta-analysis Multitreatment Studies. Medical Decision Making 1998;18:37-43. Cost population characteristics data adapted various sources: Taylor, D.H. Jr, et al. (2002). Benefits smoking cessation longevity. American Journal Public Health 2002;92(6) ASH: Action Smoking Health (2013). ASH fact sheet smoking statistics, https://ash.org.uk/files/documents/ASH_106.pdf Flack, S., et al. (2007). Cost-effectiveness interventions smoking cessation. York Health Economics Consortium, January 2007 McGhan, W.F.D., Smith, M. (1996). Pharmacoeconomic analysis smoking-cessation interventions. American Journal Health-System Pharmacy 1996;53:45-52","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","text":"Baio G. (2012). Bayesian Methods Health Economics. CRC/Chapman Hall, London","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":null,"dir":"Reference","previous_headings":"","what":"Structural Probability Sensitivity Analysis — struct.psa","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"Computes weights associated set competing models order perform structural PSA.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"","code":"struct.psa( models, effect, cost, ref = NULL, interventions = NULL, Kmax = 50000, plot = FALSE, w = NULL )"},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"models list containing output either R2jags R2WinBUGS models need combined model average effect list containing measure effectiveness computed various models (one matrix n.sim x n.ints simulations model) cost list containing measure costs computed various models (one matrix n.sim x n.ints simulations model) ref intervention considered reference strategy. default value ref=1 means intervention appearing first reference (s) () comparator(s) interventions Defines labels associated intervention. default NULL, assigns labels form \"Intervention1\", ... , \"InterventionT\" Kmax Maximum value willingness pay considered. Default value 50000. willingness pay approximated discrete grid interval [0, Kmax]. grid equal k parameter given, composed 501 elements k=NULL (default) plot logical value indicating whether function produce summary plot w vector weights. default NULL indicate function calculate model weights based DIC individual model fit. behaviour can overridden passing vector w, instance based expert opinion","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"List object bcea object, model weights DIC","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"model list containing output either R2jags R2WinBUGS models need combined model average effect list containing measure effectiveness computed various models (one matrix n_sim x n_ints simulations model) cost list containing measure costs computed various models (one matrix n_sim x n_ints simulations model).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"","code":"if (FALSE) { # load sample jags output load(system.file(\"extdata\", \"statins_base.RData\", package = \"BCEA\")) load(system.file(\"extdata\", \"statins_HC.RData\", package = \"BCEA\")) interventions <- c(\"Atorvastatin\", \"Fluvastatin\", \"Lovastatin\", \"Pravastatin\", \"Rosuvastatin\", \"Simvastatin\") m1 <- bcea(eff = statins_base$sims.list$effect, cost = statins_base$sims.list$cost.tot, ref = 1, interventions = interventions) m2 <- bcea(eff = statins_HC$sims.list$effect, cost = statins_HC$sims.list$cost.tot, ref = 1, interventions = interventions) models <- list(statins_base, statins_HC) effects <- list(statins_base$sims.list$effect, statins_HC$sims.list$effect) costs <- list(statins_base$sims.list$cost.tot, statins_HC$sims.list$cost.tot) m3 <- struct.psa(models, effects, costs, ref = 1, interventions = interventions) }"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for Objects of Class bcea — summary.bcea","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"Produces table printout summary results health economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"","code":"# S3 method for bcea summary(object, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"object bcea object containing results Bayesian modelling economic evaluation. wtp value willingness pay threshold used summary table. ... Additional arguments affecting summary produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"Prints summary table information health economic output synthetic information economic measures (EIB, CEAC, EVPI).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"","code":"data(Vaccine) he <- bcea(eff, cost, interventions = treats, ref = 2) summary(he) #> #> Cost-effectiveness analysis summary #> #> Reference intervention: Vaccination #> Comparator intervention: Status Quo #> #> Optimal decision: choose Status Quo for k < 20100 and Vaccination for k >= 20100 #> #> #> Analysis for willingness to pay parameter k = 25000 #> #> Expected net benefit #> Status Quo -36.054 #> Vaccination -34.826 #> #> EIB CEAC ICER #> Vaccination vs Status Quo 1.2284 0.529 20098 #> #> Optimal intervention (max expected net benefit) for k = 25000: Vaccination #> #> EVPI 2.4145"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"Prints summary table results mixed analysis economic evaluation given model.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"","code":"# S3 method for mixedAn summary(object, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"object object class mixedAn, results function mixedAn, generating economic evaluation set interventions, considering given market shares option. wtp value willingness pay chosen present analysis. ... Additional arguments affecting summary produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"Produces table summary information loss expected value information generated inclusion non cost-effective interventions market.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"Baio G, Russo P (2009). “decision-theoretic framework application cost-effectiveness analysis regulatory processes.” Pharmacoeconomics, 27(8), 5--16. ISSN 20356137, doi:10.1007/bf03320526 . Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000 # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) ) mixedAn(m) <- NULL # uses the results of the mixed strategy # analysis (a \"mixedAn\" object) # the vector of market shares can be defined # externally. If NULL, then each of the T # interventions will have 1/T market share # Prints a summary of the results summary(m, # uses the results of the mixed strategy analysis wtp=25000) # (a \"mixedAn\" object) #> #> Analysis of mixed strategy for willingness to pay parameter k = 25000 #> #> Reference intervention: Vaccination (50.00% market share) #> Comparator intervention: Status Quo (50.00% market share) #> #> Loss in the expected value of information = 0.61 #> # selects the relevant willingness to pay # (default: 25,000)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for Objects of Class pairwise — summary.pairwise","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"Produces table printout summary results health economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"","code":"# S3 method for pairwise summary(object, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"object pairwise object containing results Bayesian modelling economic evaluation. wtp value willingness pay threshold used summary table. ... Additional arguments affecting summary produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"Prints summary table information health economic output synthetic information economic measures (EIB, CEAC, EVPI).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"","code":"data(Vaccine) he <- bcea(eff, cost, interventions = treats, ref = 2) he_multi <- multi.ce(he) summary(he_multi) #> #> Cost-effectiveness analysis summary #> #> Intervention(s): Status Quo #> : Vaccination #> #> Optimal decision: choose Status Quo for k < 20100 and Vaccination for k >= 20100 #> #> #> Analysis for willingness to pay parameter k = 25000 #> #> Expected net benefit EIB CEAC ICER #> Status Quo -36.054 NA 0.471 NA #> Vaccination -34.826 NA 0.529 NA #> #> Optimal intervention (max expected net benefit) for k = 25000: Vaccination #> #> EVPI 2.4145"},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Dataset For ICERs From bcea Object — tabulate_means","title":"Calculate Dataset For ICERs From bcea Object — tabulate_means","text":"Calculate Dataset ICERs bcea Object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Dataset For ICERs From bcea Object — tabulate_means","text":"","code":"tabulate_means(he, comp_label = NULL, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Dataset For ICERs From bcea Object — tabulate_means","text":"bcea object containing results Bayesian modelling economic evaluation. comp_label Optional vector strings comparison labels ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Dataset For ICERs From bcea Object — tabulate_means","text":"data.frame object including mean outcomes, comparison identifier, comparison label associated ICER","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/theme_bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"bcea theme ggplot2 — theme_bcea","title":"bcea theme ggplot2 — theme_bcea","text":"bcea theme ggplot2","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/theme_bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bcea theme ggplot2 — theme_bcea","text":"","code":"theme_default() theme_ceac() theme_ceplane() theme_eib() theme_contour()"},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":null,"dir":"Reference","previous_headings":"","what":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","text":"data set contains results Bayesian analysis used model clinical output costs associated influenza vaccination.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","text":"data list including variables needed influenza vaccination. variables follows: list(\"cost\") matrix simulations posterior distribution overall costs associated two treatments list(\"c.pts\") list(\"cost.GP\") matrix simulations posterior distribution costs GP visits associated two treatments list(\"cost.hosp\") matrix simulations posterior distribution costs hospitalisations associated two treatments list(\"cost.otc\") matrix simulations posterior distribution costs --counter medications associated two treatments list(\"cost.time.\") matrix simulations posterior distribution costs time work associated two treatments list(\"cost.time.vac\") matrix simulations posterior distribution costs time needed get vaccination associated two treatments list(\"cost.travel\") matrix simulations posterior distribution costs travel get vaccination associated two treatments list(\"cost.trt1\") matrix simulations posterior distribution overall costs first line treatment associated two interventions list(\"cost.trt2\") matrix simulations posterior distribution overall costs second line treatment associated two interventions list(\"cost.vac\") matrix simulations posterior distribution costs vaccination list(\"eff\") matrix simulations posterior distribution clinical benefits associated two treatments list(\"e.pts\") list(\"N\") number subjects reference population list(\"N.outcomes\") number clinical outcomes analysed list(\"N.resources\") number health-care resources study list(\"QALYs.adv\") vector posterior distribution QALYs associated advert events list(\"QALYs.death\") vector posterior distribution QALYs associated death list(\"QALYs.hosp\") vector posterior distribution QALYs associated hospitalisation list(\"QALYs.inf\") vector posterior distribution QALYs associated influenza infection list(\"QALYs.pne\") vector posterior distribution QALYs associated pneumonia list(\"treats\") vector labels associated two treatments list(\"vaccine_mat\") matrix containing simulations parameters used original model","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","text":"Adapted Turner D, Wailoo , Cooper N, Sutton , Abrams K, Nicholson K. cost-effectiveness influenza vaccination healthy adults 50-64 years age. Vaccine. 2006;24:1035-1043.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","text":"Baio, G., Dawid, . P. (2011). Probabilistic Sensitivity Analysis Health Economics. Statistical Methods Medical Research doi:10.1177/0962280211419832.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate bcea — validate_bcea","title":"Validate bcea — validate_bcea","text":"Validate bcea","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate bcea — validate_bcea","text":"","code":"validate_bcea(eff, cost, ref, interventions)"},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate bcea — validate_bcea","text":"eff Effectiveness matrix cost Cost matrix ref Reference intervention interventions interventions","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate EIB parameters — validate_eib_params","title":"Validate EIB parameters — validate_eib_params","text":"Validate EIB parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate EIB parameters — validate_eib_params","text":"","code":"validate_eib_params(params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate EIB parameters — validate_eib_params","text":"params Graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate EIB parameters — validate_eib_params","text":"List graph parameters","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-245","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.5","title":"BCEA 2.4.5","text":"October 2023 Moved internal EVPPI calculation BCEA now uses voi package instead. Refactoring retaining interface functionality. evppi() tested use cases BCEA book (1c1457d2) Select parameters position (well name) new evppi() (f2e4d005) Use single parameter case like voi package methods sal (#140) New evppi() matching output old evppi() (1e2c5e7) Latest development version voi needed use check = TRUE voi::evppi() order access fitting data (6e436b5, 94f5fc5) longer require INLA package available inside BCEA can remove direct dependency. helps passing CRAN checks GitHub Actions ()","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-244","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.4","title":"BCEA 2.4.4","text":"June 2023 Patch fix CRAN checks error. Suggested package MCMCvis wasn’t used conditionally unit test. Moved Required packages DESCRIPTION.","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-243","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.3","title":"BCEA 2.4.3","text":"May 2023","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bug-fixes-2-4-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"BCEA 2.4.3","text":"Consistent colours across plots intervention grid plots plot.bcea() (cf1ee43) make.report() change variable name (f940f2e) Fixed issue summary table names interventions wrong order (6a006e3) summary.bcea() now prints results chosen comparisons always . kstar best bcea() object updated subset interventions (#125)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"refactoring-2-4-3","dir":"Changelog","previous_headings":"","what":"Refactoring","title":"BCEA 2.4.3","text":"withr::with_par() used plotting function plot.bcea() temporarily change graphics parameters. (725c536) Using @md markdown syntax function documentation Update psa.struct() add absolute value formula compute weights (1cea278) Use dplyr piping new syntax .data$* simply using speech marks \"*\" (2b280ad)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"miscellaneous-2-4-3","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"BCEA 2.4.3","text":"Template added GitHub Issues (0ea59fa)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-242","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.2","title":"BCEA 2.4.2","text":"August 2022","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bug-fixes-2-4-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"BCEA 2.4.2","text":"summary.bcea() wasn’t passing wtp argument sim_table() internally (5440eb3) summary() basic bcea multi.ce objects. Now summary.pairwise() method. (88ade51) struct.psa() output now works summary() plots still work without use $ get bcea object . (b014c83) Changed wtp argument bcea() k wtp plotting functions refers wtp line scalar whereas k grid points. Added error message use new argument. (b014c83) bcea() still allows scalar k added warning give empty plots. Updated GitHub Actions checking package use r-lib/Actions version 2. error finding INLA solved Gabor RStudio (see thread https://community.rstudio.com/t/-finding-inla-package---cran--actions/141398) GrassmannOptim package r-release-macos-x86_64 isn’t available resulting CRAN check error doesn’t appear maintained. Tried emailing author bounced. Removed dependency copied GrassmannOptim() function inside package acknowledgement.","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"refactoring-2-4-2","dir":"Changelog","previous_headings":"","what":"Refactoring","title":"BCEA 2.4.2","text":"Now uses Rdpack bibliography documentation (229c96d) cost health values Smoking Vaccine data sets renamed c e cost eff. avoid conflict c() function. Changed axes labels cost-effectiveness planes “differential” “incremental”. (688d98b)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"new-features-2-4-2","dir":"Changelog","previous_headings":"","what":"New features","title":"BCEA 2.4.2","text":"Can now specify order interventions labels legend ce plane (contour plots) base R ggplot2 .e. reference first second optional ref_first argument (cc38f07) Can specify currency axes ceplane.plot() ceac.plot() ggplot2 versions (6808aa6) Argument added ceplane.plot() icer_annot annotate ICER points text label intervention name. ggplot2 moment. (a7b4beb) Added pos argument contour2() consistent contour() ceplane.plot(). (50f8f8b) Allow passing ref argument name well index bcea(). (9eab459)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-2412","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.1.2","title":"BCEA 2.4.1.2","text":"April 2022","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bug-fixes-2-4-1-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"BCEA 2.4.1.2","text":"ceplane_ggplot() missing legend Legend bug evppi() Arguments consistent order ceplane.plot() ceplane_plot_base() wasn’t showing grey area. Fixed removing alpha transparency ceac.plot() wasn’t showing confidence interval default one comparison Typo fixed dropping dimension compute_vi() setReferenceGroup() CEAC plot legend error; doesn’t use supplied names generic intervention 1, intervention 2, … (#82) Missing multi.ce() line reference group (#80)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"miscellaneous-2-4-1-2","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"BCEA 2.4.1.2","text":"Use cli package warning messages Removed internal helper functions Manual using @keyword internal Refactor contour plots Extend function (ceac.plot()) take style arguments e.g. colour lines, types points line thickness. Resuse ceplane.plot() code contour() line length, seq_len(), remove ; Contributor guidelines (#93) Deprecated functions document contour2() changed xlim, ylim arguments optional; ceplane.plot() since passes contour() ceplane.plot() vignettes written","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-2411","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.1.1","title":"BCEA 2.4.1.1","text":"Oct 2021","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"major-refactoring-2-4-1-1","dir":"Changelog","previous_headings":"","what":"Major refactoring","title":"BCEA 2.4.1.1","text":"Code base improved robustness extensibility. bcea() now helper function calls constructor new_bcea(), separating concerns. new_bcea() composed smaller HEE statistics functions names starting compute_* e.g. compute_CEAC(), compute_EIB(),…. allows us call test individually. also allows flexibility changing adding functionality new_bcea(). Plotting functions rewritten. functions now simply dispatch base R, ggplot2 plotly versions (think strategy pattern). Internally, functions, e.g.ceplane_plot_ggplot(), also split parameter data setting plotting components. modulisation allows us add new layers plots modify existing parameter sets defaults. also return data without plotting step e.g. ggplot2::autoplot(). also means can reuse functionality across plots axes legend setting e.g. BCEA:::where_legend(). Deprecated mce.plot(). Now dispatched ceac.plot() multi.ce() bcea() outputs. multiple comparison plot pairwise comparison interventions returned default. alternative version comparison reference group still available. Plots tables using S3 methods bcea type object. Tables updated. Duplication summary() sim_table() removed. createInputs() used EVPI calculation now dispatches S3 methods JAGS, BUGS, Stan R data types. make.report() rewritten separate section files.","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"new-features-2-4-1-1","dir":"Changelog","previous_headings":"","what":"New features","title":"BCEA 2.4.1.1","text":"Extend ways set comparison interventions. Subsets comparison can still set call plotting function . Now subsets can set original bcea() construction separately using setter functions setComparisons(). Similarly, maximum willingness pay reference group can set setKmax() setReferenceGroup(), respectively. multi.ce() CEriskAv() also now work similarly. operate modifying bcea object, rather creating new one (think decorator pattern). bcea() methods JAGS, WinBUGS, Stan (#76)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"miscellaneous-2-4-1-1","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"BCEA 2.4.1.1","text":"Additional help documentation examples. New vignettes plotting comparison intervention setting. Testing suite started. comprehensive yet. Added NEWS.md file track changes package. Details previous releases, dates, versions, fixes enhancements obtained CRAN code comments little patchy. pkgdown GitHub site made. Cheatsheet written published RStudio site (#22). Dependency package ldr removed BCEA removed CRAN (#74)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-23-11","dir":"Changelog","previous_headings":"","what":"BCEA 2.3-1.1","title":"BCEA 2.3-1.1","text":"26 Aug 2019","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-23-1","dir":"Changelog","previous_headings":"","what":"BCEA 2.3-1","title":"BCEA 2.3-1","text":"5 Aug 2019","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-6","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-6","title":"BCEA 2.2-6","text":"11 July 2018 Fix evppi allow N selected methods Fix diag.evppi","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-5","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-5","title":"BCEA 2.2-5","text":"18 Nov 2016 changes EVPPI","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-224","dir":"Changelog","previous_headings":"","what":"BCEA 2.2.4","title":"BCEA 2.2.4","text":"Nov 2016 Fixes new ggplot2 version (legend.spacing() plot.title hjust argument)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-3","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-3","title":"BCEA 2.2-3","text":"22 May 2016 Major update EVPPI include PFC Fixed issues info.rank","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-2","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-2","title":"BCEA 2.2-2","text":"25 Jan 2016 Minor change ceef.plot align ggplot2 v2.0.0","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-221","dir":"Changelog","previous_headings":"","what":"BCEA 2.2.1","title":"BCEA 2.2.1","text":"Oct 2015 Adds info-rank plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22","dir":"Changelog","previous_headings":"","what":"BCEA 2.2","title":"BCEA 2.2","text":"Oct 2015 Cleaned aligned R’s settings EVPPI function polished ","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-21-1","dir":"Changelog","previous_headings":"","what":"BCEA 2.1-1","title":"BCEA 2.1-1","text":"6 May 2015 2015 New function EVPPI using SPDE-INLA Modifications EVPPI functions Documentation updated Allows xlim & ylim ceplane.plot(), contour() contour2() functions now possible run bcea scalar wtp Old evppi() function method renamed evppi0, means ’s also new plot.evppi0 method","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-21-0","dir":"Changelog","previous_headings":"","what":"BCEA 2.1-0","title":"BCEA 2.1-0","text":"13 Jan 2015 Migrated (require()) (requireNamespace(,quietly=TRUE)) Documentation updated Added threshold argument ceef.plot function","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-210-pre2","dir":"Changelog","previous_headings":"","what":"BCEA 2.1.0-pre2","title":"BCEA 2.1.0-pre2","text":"Oct 2014 modifications ceef.plot, createInputs, struct.psa","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-21-0-pre1","dir":"Changelog","previous_headings":"","what":"BCEA 2.1-0-pre1","title":"BCEA 2.1-0-pre1","text":"13 Jan 2015 Documentation updated Smoking dataset ceef.plot function included, additional modifications","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20-2c","dir":"Changelog","previous_headings":"","what":"BCEA 2.0-2c","title":"BCEA 2.0-2c","text":"2 Dec 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-v20-2b","dir":"Changelog","previous_headings":"","what":"BCEA v2.0-2b","title":"BCEA v2.0-2b","text":"2 Dec 2013 ceac.plot eib.plot: option comparison included base graphics","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20-2","dir":"Changelog","previous_headings":"","what":"BCEA 2.0-2","title":"BCEA 2.0-2","text":"2 Dec 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20-1","dir":"Changelog","previous_headings":"","what":"BCEA 2.0-1","title":"BCEA 2.0-1","text":"31 July 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20","dir":"Changelog","previous_headings":"","what":"BCEA 2.0","title":"BCEA 2.0","text":"30 July 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"feature-updates-2-0","dir":"Changelog","previous_headings":"","what":"Feature updates","title":"BCEA 2.0","text":"Implements two quick general methods compute EVPPI Function CreateInputs(), takes input object class rjags bugs Compute EVPPI one parameters calling function evppi() Results can visualised using specific method plot class evppi show overall EVPI EVPPI selected parameter(s)","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-13-0","dir":"Changelog","previous_headings":"","what":"BCEA 1.3-0","title":"BCEA 1.3-0","text":"3 July 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-12","dir":"Changelog","previous_headings":"","what":"BCEA 1.2","title":"BCEA 1.2","text":"17 September 2012","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-111","dir":"Changelog","previous_headings":"","what":"BCEA 1.1.1","title":"BCEA 1.1.1","text":"22 Feb 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-11","dir":"Changelog","previous_headings":"","what":"BCEA 1.1","title":"BCEA 1.1","text":"15 Sept 2012","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-10","dir":"Changelog","previous_headings":"","what":"BCEA 1.0","title":"BCEA 1.0","text":"13 May 2012","code":""}]
+[{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Cost-Effectiveness Acceptability Curve Plots","text":"intention vignette show plot different styles cost-effectiveness acceptability curves using BCEA package.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"two-interventions-only","dir":"Articles","previous_headings":"","what":"Two interventions only","title":"Cost-Effectiveness Acceptability Curve Plots","text":"simplest case, usually alternative intervention (\\(=1\\)) versus status-quo (\\(=0\\)). plot show probability alternative intervention cost-effective willingness pay, \\(k\\), \\[ p(NB_1 \\geq NB_0 | k) \\mbox{ } NB_i = ke - c \\] Using set \\(N\\) posterior samples, approximated \\[ \\frac{1}{N} \\sum_j^N \\mathbb{} (k \\Delta e^j - \\Delta c^j) \\]","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"r-code","dir":"Articles","previous_headings":"Two interventions only","what":"R code","title":"Cost-Effectiveness Acceptability Curve Plots","text":"calculate BCEA use bcea() function. plot defaults base R plotting. Type plot can set explicitly using graph argument. plotting arguments can specified title, line colours theme.","code":"data(\"Vaccine\") he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention. # str(he) ceac.plot(he) ceac.plot(he, graph = \"base\") ceac.plot(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") ceac.plot(he, graph = \"ggplot2\", title = \"my title\", line = list(color = \"green\"), theme = theme_dark())"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Cost-Effectiveness Acceptability Curve Plots","text":"situation two interventions consider. Incremental values can obtained either always fixed reference intervention, status-quo, pair-wise comparisons.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"against-a-fixed-reference-intervention","dir":"Articles","previous_headings":"Multiple interventions","what":"Against a fixed reference intervention","title":"Cost-Effectiveness Acceptability Curve Plots","text":"Without loss generality, assume interested intervention \\(=1\\), wish calculate \\[ p(NB_1 \\geq NB_s | k) \\;\\; \\exists \\; s \\S \\] Using set \\(N\\) posterior samples, approximated \\[ \\frac{1}{N} \\sum_j^N \\mathbb{} (k \\Delta e_{1,s}^j - \\Delta c_{1,s}^j) \\]","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions > Against a fixed reference intervention","what":"R code","title":"Cost-Effectiveness Acceptability Curve Plots","text":"default plot ceac.plot() simply follow steps new data set. Reposition legend. Define colour palette.","code":"data(\"Smoking\") he <- bcea(eff, cost, ref = 4) # str(he) ceac.plot(he) ceac.plot(he, graph = \"base\", title = \"my title\", line = list(color = \"green\")) ceac.plot(he, graph = \"ggplot2\", title = \"my title\", line = list(color = \"green\")) ceac.plot(he, pos = FALSE) # bottom right ceac.plot(he, pos = c(0, 0)) ceac.plot(he, pos = c(0, 1)) ceac.plot(he, pos = c(1, 0)) ceac.plot(he, pos = c(1, 1)) ceac.plot(he, graph = \"ggplot2\", pos = c(0, 0)) ceac.plot(he, graph = \"ggplot2\", pos = c(0, 1)) ceac.plot(he, graph = \"ggplot2\", pos = c(1, 0)) ceac.plot(he, graph = \"ggplot2\", pos = c(1, 1)) mypalette <- RColorBrewer::brewer.pal(3, \"Accent\") ceac.plot(he, graph = \"base\", title = \"my title\", line = list(color = mypalette), pos = FALSE) ceac.plot(he, graph = \"ggplot2\", title = \"my title\", line = list(color = mypalette), pos = FALSE)"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"pair-wise-comparisons","dir":"Articles","previous_headings":"Multiple interventions","what":"Pair-wise comparisons","title":"Cost-Effectiveness Acceptability Curve Plots","text":", without loss generality, assume interested intervention \\(=1\\), wish calculate \\[ p(NB_1 = \\max\\{NB_i : \\S\\} | k) \\] can approximated following. \\[ \\frac{1}{N} \\sum_j^N \\prod_{\\S} \\mathbb{} (k \\Delta e_{1,}^j - \\Delta c_{1,}^j) \\]","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"r-code-2","dir":"Articles","previous_headings":"Multiple interventions > Pair-wise comparisons","what":"R code","title":"Cost-Effectiveness Acceptability Curve Plots","text":"BCEA first must determine combinations paired interventions using multi.ce() function. can use plotting calls .e. ceac.plot() BCEA deal pairwise situation appropriately. Note case probabilities given willingness pay sum 1. line width can changes either single value change lines thickness value .","code":"he <- multi.ce(he) ceac.plot(he, graph = \"base\") ceac.plot(he, graph = \"base\", title = \"my title\", line = list(color = \"green\"), pos = FALSE) mypalette <- RColorBrewer::brewer.pal(4, \"Dark2\") ceac.plot(he, graph = \"base\", title = \"my title\", line = list(color = mypalette), pos = c(0,1)) ceac.plot(he, graph = \"ggplot2\", title = \"my title\", line = list(color = mypalette), pos = c(0,1)) ceac.plot(he, graph = \"ggplot2\", title = \"my title\", line = list(size = 2)) ceac.plot(he, graph = \"ggplot2\", title = \"my title\", line = list(size = c(1,2,3)))"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Cost-Effectiveness Efficiency Frontier","text":"line connecting successive points cost-effectiveness plane represent effect cost associated different treatment alternatives. gradient line segment represents ICER treatment comparison two alternatives represented segment. cost-effectiveness frontier consists set points corresponding treatment alternatives considered cost-effective different values cost-effectiveness threshold. steeper gradient successive points frontier, higher ICER treatment alternatives expensive alternative considered cost-effective high value cost-effectiveness threshold assumed. Points lying cost-effectiveness frontier represent treatment alternatives considered cost-effective value cost-effectiveness threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"r-code","dir":"Articles","previous_headings":"","what":"R code","title":"Cost-Effectiveness Efficiency Frontier","text":"create plots BCEA first call bcea() function. base R ggplot Check legend position argument:","code":"data(Smoking) treats <- c(\"No intervention\", \"Self-help\", \"Individual counselling\", \"Group counselling\") bcea_smoke <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500) # all interventions ceef.plot(bcea_smoke) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.28824 45.733 158.66 1.5645 #> Group counselling 0.72252 143.301 224.67 1.5663 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0.00000 0.000 Extended dominance #> Individual counselling 0.48486 94.919 Extended dominance # subset setComparisons(bcea_smoke) <- c(1,3) ceef.plot(bcea_smoke) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance # check numbering and legend setComparisons(bcea_smoke) <- c(3,1) ceef.plot(bcea_smoke) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0.48486 94.919 Extended dominance setComparisons(bcea_smoke) <- c(3,2) ceef.plot(bcea_smoke) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Individual counselling 0.43428 97.568 224.67 1.5663 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0.19662 49.186 Extended dominance #> Self-help 0.00000 0.000 Extended dominance setComparisons(bcea_smoke) <- 1 ceef.plot(bcea_smoke) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.72252 143.3 198.33 1.5658 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance # add interventions back in setComparisons(bcea_smoke) <- c(1,3) ceef.plot(bcea_smoke) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance bcea_smoke <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500) # all interventions ceef.plot(bcea_smoke, graph = \"ggplot\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.28824 45.733 158.66 1.5645 #> Group counselling 0.72252 143.301 224.67 1.5663 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0.00000 0.000 Extended dominance #> Individual counselling 0.48486 94.919 Extended dominance # subset setComparisons(bcea_smoke) <- c(1,3) ceef.plot(bcea_smoke, graph = \"ggplot\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance # check numbering and legend setComparisons(bcea_smoke) <- c(3,1) ceef.plot(bcea_smoke, graph = \"ggplot\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0.48486 94.919 Extended dominance setComparisons(bcea_smoke) <- c(3,2) ceef.plot(bcea_smoke, graph = \"ggplot\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Individual counselling 0.43428 97.568 224.67 1.5663 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0.19662 49.186 Extended dominance #> Self-help 0.00000 0.000 Extended dominance setComparisons(bcea_smoke) <- 1 ceef.plot(bcea_smoke, graph = \"ggplot\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.72252 143.3 198.33 1.5658 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance # add interventions back in setComparisons(bcea_smoke) <- c(1,3) ceef.plot(bcea_smoke, graph = \"ggplot\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance # base R ceef.plot(bcea_smoke, pos = c(1,0)) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, pos = c(1,1)) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, pos = TRUE) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, pos = FALSE) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, pos = \"topleft\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, pos = \"topright\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, pos = \"bottomleft\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, pos = \"bottomright\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance # ggplot2 ceef.plot(bcea_smoke, graph = \"ggplot\", pos = c(1,0)) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = c(1,1)) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = TRUE) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = FALSE) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = \"top\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = \"bottom\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = \"left\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = \"right\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"flipping-plot","dir":"Articles","previous_headings":"R code","what":"Flipping plot","title":"Cost-Effectiveness Efficiency Frontier","text":"","code":"ceef.plot(bcea_smoke, flip = TRUE, dominance = FALSE, start.from.origins = FALSE, print.summary = FALSE, graph = \"base\") ceef.plot(bcea_smoke, dominance = TRUE, start.from.origins = FALSE, pos = TRUE, print.summary = FALSE, graph = \"ggplot2\")"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"start-from-origin-or-smallest-ec-","dir":"Articles","previous_headings":"R code","what":"Start from origin or smallest (e,c).","title":"Cost-Effectiveness Efficiency Frontier","text":"","code":"ceef.plot(bcea_smoke, flip = TRUE, dominance = TRUE, start.from.origins = TRUE, print.summary = FALSE, graph = \"base\") ceef.plot(bcea_smoke, dominance = TRUE, start.from.origins = TRUE, pos = TRUE, print.summary = FALSE, graph = \"ggplot2\")"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"negative-cost-or-effectiveness","dir":"Articles","previous_headings":"R code","what":"Negative cost or effectiveness","title":"Cost-Effectiveness Efficiency Frontier","text":"","code":"data(\"Smoking\") cost[, 4] <- -cost[, 4] bcea_smoke <- bcea(eff, cost, ref = 3, interventions = treats, Kmax = 500) # all interventions ceef.plot(bcea_smoke, graph = \"ggplot\") #> Costs are negative, the frontier will not start from the origins #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Group counselling 1.133 -143.3 NA NA #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.41051 0.000 Absolute dominance #> Self-help 0.69875 45.733 Absolute dominance #> Individual counselling 1.13303 -143.301 Extended dominance ceef.plot(bcea_smoke, graph = \"base\") #> Costs are negative, the frontier will not start from the origins #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Group counselling 1.133 -143.3 NA NA #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.41051 0.000 Absolute dominance #> Self-help 0.69875 45.733 Absolute dominance #> Individual counselling 1.13303 -143.301 Extended dominance ceef.plot(bcea_smoke, start.from.origins = TRUE, graph = \"ggplot\") #> Costs are negative, the frontier will not start from the origins #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Group counselling 1.133 -143.3 NA NA #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.41051 0.000 Absolute dominance #> Self-help 0.69875 45.733 Absolute dominance #> Individual counselling 1.13303 -143.301 Extended dominance ceef.plot(bcea_smoke, start.from.origins = TRUE, graph = \"base\") #> Costs are negative, the frontier will not start from the origins #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Group counselling 1.133 -143.3 NA NA #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.41051 0.000 Absolute dominance #> Self-help 0.69875 45.733 Absolute dominance #> Individual counselling 1.13303 -143.301 Extended dominance setComparisons(bcea_smoke) <- c(1,2) ceef.plot(bcea_smoke, graph = \"ggplot\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.28824 45.733 158.66 1.5645 #> Individual counselling 0.48486 94.919 250.16 1.5668 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0 0 Extended dominance ceef.plot(bcea_smoke, graph = \"base\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.28824 45.733 158.66 1.5645 #> Individual counselling 0.48486 94.919 250.16 1.5668 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0 0 Extended dominance eff[, 3] <- -eff[, 3] bcea_smoke <- bcea(eff, cost, ref = 3, interventions = treats, Kmax = 500) ceef.plot(bcea_smoke, graph = \"ggplot\") #> Costs and benefits are negative, the frontier will not start from the origins #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Group counselling 1.133 -143.3 NA NA #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.41051 0.000 Absolute dominance #> Self-help 0.69875 45.733 Absolute dominance #> Individual counselling 1.13303 -143.301 Extended dominance ceef.plot(bcea_smoke, graph = \"base\") #> Costs and benefits are negative, the frontier will not start from the origins #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Group counselling 1.133 -143.3 NA NA #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.41051 0.000 Absolute dominance #> Self-help 0.69875 45.733 Absolute dominance #> Individual counselling 1.13303 -143.301 Extended dominance data(\"Smoking\") eff[, 3] <- -eff[, 3] bcea_smoke <- bcea(eff, cost, ref = 3, interventions = treats, Kmax = 500) ceef.plot(bcea_smoke, graph = \"ggplot\") #> Benefits are negative, the frontier will not start from the origins #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Individual counselling -0.89536 94.919 NA NA #> Group counselling 1.13303 143.301 23.852 1.5289 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.41051 0.000 Absolute dominance #> Self-help 0.69875 45.733 Absolute dominance ceef.plot(bcea_smoke, graph = \"base\") #> Benefits are negative, the frontier will not start from the origins #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Individual counselling -0.89536 94.919 NA NA #> Group counselling 1.13303 143.301 23.852 1.5289 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.41051 0.000 Absolute dominance #> Self-help 0.69875 45.733 Absolute dominance"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceplane.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Cost-effectiveness plane","text":"intention vignette show plot different styles cost-effectiveness acceptability curves using BCEA package.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceplane.html","id":"r-code","dir":"Articles","previous_headings":"Introduction","what":"R code","title":"Cost-effectiveness plane","text":"calculate BCEA use bcea() function. plot defaults base R plotting. Type plot can set explicitly using graph argument. plotting arguments can specified title, line colours theme. mean point can suppress sample points passing size NA.","code":"data(\"Vaccine\") he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ceplane.plot(he, graph = \"base\") ceplane.plot(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") ceplane.plot(he, graph = \"ggplot2\", title = \"my title\", line = list(color = \"green\", size = 3), point = list(color = \"blue\", shape = 10, size = 5), icer = list(color = \"orange\", size = 5), area = list(fill = \"grey\"), theme = theme_linedraw()) ceplane.plot(he, graph = \"ggplot2\", point = list(size = NA), icer = list(size = 5)) #> Warning: Removed 1000 rows containing missing values (`geom_point()`)."},{"path":"https://n8thangreen.github.io/BCEA/articles/ceplane.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Cost-effectiveness plane","text":"situation two interventions consider.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceplane.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions","what":"R code","title":"Cost-effectiveness plane","text":"Reposition legend.","code":"data(\"Smoking\") he <- bcea(eff, cost, ref = 4) # str(he) ceplane.plot(he) ceplane.plot(he, graph = \"ggplot2\") ceplane.plot(he, graph = \"ggplot2\", title = \"my title\", line = list(color = \"red\", size = 1), point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2), icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5)) ceplane.plot(he, pos = FALSE) # bottom right ceplane.plot(he, pos = c(0, 0)) ceplane.plot(he, pos = c(0, 1)) ceplane.plot(he, pos = c(1, 0)) ceplane.plot(he, pos = c(1, 1))"},{"path":"https://n8thangreen.github.io/BCEA/articles/contour.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Contour Plots","text":"intention vignette show plot different styles contour plot using BCEA package contour() contour2() functions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/contour.html","id":"r-code","dir":"Articles","previous_headings":"Introduction","what":"R code","title":"Contour Plots","text":"calculate BCEA use bcea() function. plot defaults base R plotting. Type plot can set explicitly using graph argument. User-defined contour levels can provided. levels nlevels arguments specify quantiles number levels. base R levels arguments kept back-compatibility ggplot2 style arguments used associated plot. plotting arguments can specified title, line colour thickness type point. Alternatively, contour2() function essentially wrapper ceplane.plot() addition contour lines. plotting arguments can specified exactly way .","code":"data(\"Vaccine\") he <- bcea(eff, cost, ref = 2) contour(he, graph = \"base\") contour(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") contour(he, levels = c(0.2, 0.8)) contour(he, graph = \"ggplot2\", contour = list(breaks = c(0.2, 0.8))) contour(he, graph = \"ggplot2\", title = \"my title\", point = list(color = \"blue\", shape = 2, size = 5), contour = list(size = 2)) contour(he, graph = \"base\", title = \"my title\", point = list(color = \"blue\", shape = 2, size = 2), contour = list(size = 2)) contour2(he, graph = \"base\") contour2(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") contour2(he, graph = \"ggplot2\", title = \"my title\", point = list(color = \"blue\", shape = 10, size = 5), contour = list(size = 2)) contour2(he, graph = \"base\", title = \"my title\", point = list(color = \"blue\", shape = 2, size = 3), contour = list(size = 4))"},{"path":"https://n8thangreen.github.io/BCEA/articles/contour.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Contour Plots","text":"situation two interventions consider.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/contour.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions","what":"R code","title":"Contour Plots","text":"multiple groups quadrant annotation omitted. scale argument determines smoothness contours. quantiles number levels. , applies contour2() version contour plot . styling plot multiple comparisons can specifically change colour point type comparison. Reposition legend.","code":"data(\"Smoking\") he <- bcea(eff, cost, ref = 4) # str(he) contour(he) contour(he, graph = \"ggplot2\") contour(he, scale = 0.9) contour(he, graph = \"ggplot2\", scale = 0.9) ##TODO: what is the equivalent ggplot2 argument? contour(he, nlevels = 10) contour(he, graph = \"ggplot2\", contour = list(bins = 10)) contour(he, levels = c(0.2, 0.8)) contour(he, graph = \"ggplot2\", contour = list(breaks = c(0.2, 0.8))) contour(he, graph = \"ggplot2\", title = \"my title\", line = list(color = \"red\", size = 1), point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2), icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5), contour = list(size = 2)) contour(he, graph = \"base\", title = \"my title\", line = list(color = \"red\", size = 1), point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2), icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5), contour = list(size = 4)) contour2(he, wtp = 250) contour2(he, wtp = 250, graph = \"ggplot2\") contour2(he, wtp = 250, graph = \"ggplot2\", title = \"my title\", line = list(color = \"red\", size = 1), point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2), icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5), contour = list(size = 2)) contour2(he, wtp = 250, graph = \"base\", title = \"my title\", line = list(color = \"red\", size = 1), point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2), icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5), contour = list(size = 4)) contour(he, pos = FALSE) # bottom right contour(he, pos = c(0, 0)) contour(he, pos = c(0, 1)) contour(he, pos = c(1, 0)) contour(he, pos = c(1, 1))"},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Expected Incremental Benefit Plot","text":"intention vignette show plot different styles expected incremental benefit (EIB) plots using BCEA package.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"two-interventions-only","dir":"Articles","previous_headings":"","what":"Two interventions only","title":"Expected Incremental Benefit Plot","text":"simplest case, usually alternative intervention (\\(=1\\)) versus status-quo (\\(=0\\)). plot based incremental benefit function willingness pay \\(k\\). \\[ IB(\\theta) = k \\Delta_e - \\Delta_c \\] Using set \\(S\\) posterior samples, EIB approximated \\[ \\frac{1}{S} \\sum_s^S IB(\\theta_s) \\] \\(\\theta_s\\) realised configuration parameters \\(\\theta\\) correspondence \\(s\\)-th simulation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"r-code","dir":"Articles","previous_headings":"Two interventions only","what":"R code","title":"Expected Incremental Benefit Plot","text":"calculate BCEA use bcea() function. default EIB plot gives single diagonal line using base R. vertical line represents break-even value corresponding \\(k^*\\) indicating threshold alternative treatment cost-effective status-quo. \\[ k^* = \\min\\{ k : \\mbox{EIB} > 0 \\} \\] point curve crosses x-axis. plot defaults base R plotting. Type plot can set explicitly using graph argument. plotting arguments can specified title, line colours theme. Credible interval can also plotted using plot.cri logical argument.","code":"data(Vaccine) he <- bcea(eff, cost, ref = 2, interventions = treats, Kmax = 50000, plot = FALSE) eib.plot(he) eib.plot(he, graph = \"base\") eib.plot(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") eib.plot(he, graph = \"ggplot2\", main = \"my title\", line = list(color = \"green\"), theme = theme_dark()) eib.plot(he, plot.cri = FALSE)"},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Expected Incremental Benefit Plot","text":"situation two interventions consider. Incremental values can obtained either always fixed reference intervention, status quo, comparisons simultaneously. curves pair-wise comparisons status-quo vertical lines k* annotation simultaneous comparisons. Without loss generality, assume status quo intervention \\(=0\\), wish calculate \\[ \\frac{1}{S} \\sum_s^S IB(\\theta^{i0}_s) \\;\\; \\mbox{} \\; \\] break-even points represent preference two best interventions \\(k\\). \\[ k^*_i = \\min\\{ k : \\mbox{EIB}(\\theta^) > \\mbox{EIB}(\\theta^j) \\} \\] right-curves cross x-axis.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions","what":"R code","title":"Expected Incremental Benefit Plot","text":"default plot eib.plot() simply follow steps new data set. example, can change main title EIB line colours green. Credible interval can also plotted . isn’t recommended case since hard understand many lines.","code":"data(Smoking) treats <- c(\"No intervention\", \"Self-help\", \"Individual counselling\", \"Group counselling\") he <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500) eib.plot(he) eib.plot(he, graph = \"base\", main = \"my title\", line = list(color = \"green\")) eib.plot(he, graph = \"ggplot2\", main = \"my title\", line = list(color = \"green\")) eib.plot(he, plot.cri = TRUE)"},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"repositioning-the-legend-","dir":"Articles","previous_headings":"Multiple interventions > R code","what":"Repositioning the legend.","title":"Expected Incremental Benefit Plot","text":"base R, ggplot2, Define colour palette different colour EIB line.","code":"eib.plot(he, pos = FALSE) # bottom right eib.plot(he, pos = c(0, 0)) eib.plot(he, pos = c(0, 1)) eib.plot(he, pos = c(1, 0)) eib.plot(he, pos = c(1, 1)) ##TODO: eib.plot(he, graph = \"ggplot2\", pos = c(0, 0)) eib.plot(he, graph = \"ggplot2\", pos = c(0, 1)) eib.plot(he, graph = \"ggplot2\", pos = c(1, 0)) eib.plot(he, graph = \"ggplot2\", pos = c(1, 1)) mypalette <- RColorBrewer::brewer.pal(3, \"Accent\") eib.plot(he, graph = \"base\", line = list(color = mypalette)) eib.plot(he, graph = \"ggplot2\", line = list(color = mypalette))"},{"path":"https://n8thangreen.github.io/BCEA/articles/info-rank.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Info-rank plot","text":"smaller number top variables can selected using howManyPars argument.","code":"data(\"Vaccine\") m <- bcea(e, c) inp <- createInputs(vaccine, print_is_linear_comb = FALSE) info.rank(m, inp) info.rank(m, inp, graph = \"base\") info.rank(m, inp, graph = \"plotly\") info.rank(m, inp, graph = \"base\", howManyPars = 10) info.rank(m, inp, graph = \"plotly\", howManyPars = 10)"},{"path":"https://n8thangreen.github.io/BCEA/articles/paired_vs_multiple_comps.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Paired vs Multiple Comparisons","text":"intention vignette show plot CEAC EIB plots depending whether consider interventions simultaneously pair-wise reference.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/paired_vs_multiple_comps.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Paired vs Multiple Comparisons","text":"situation two interventions consider. Incremental values can obtained either always fixed reference intervention, status-quo, comparisons simultaneously. call paired comparison multiple comparison.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/articles/paired_vs_multiple_comps.html","id":"r-code","dir":"Articles","previous_headings":"Multiple interventions > Against a fixed reference intervention","what":"R code","title":"Paired vs Multiple Comparisons","text":"default plot ceac.plot() simply follow steps new data set.","code":"data(\"Smoking\") he <- bcea(eff, cost, ref = 4, Kmax = 500) par(mfrow = c(2,1)) ceac.plot(he) abline(h = 0.5, lty = 2) abline(v = c(160, 225), lty = 3) eib.plot(he, plot.cri = FALSE)"},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/articles/paired_vs_multiple_comps.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions > Pair-wise comparisons","what":"R code","title":"Paired vs Multiple Comparisons","text":"BCEA first must determine combinations paired interventions using multi.ce() function.","code":"he.multi <- multi.ce(he) par(mfrow = c(2, 1)) ceac.plot(he.multi) abline(h = 0.5, lty = 2) abline(v = c(160, 225), lty = 3) eib.plot(he, plot.cri = FALSE)"},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/articles/R_journal_examples.html","id":"smoking-cessation-example","dir":"Articles","previous_headings":"","what":"Smoking cessation example","title":"R journal examples","text":"grid plots individual plots cost-effectiveness plane plots multiple simultaneous comparisons mixed strategy risk aversion","code":"data(Smoking) treats <- c(\"No intervention\", \"Self-help\", \"Individual counselling\", \"Group counselling\") bcea_smoke <- bcea(e, c, ref = 4, interventions = treats, Kmax = 500) library(ggplot2) plot(bcea_smoke) plot(bcea_smoke, graph = \"ggplot2\", wtp = 250, pos = TRUE, size = rel(2), ICER.size = 2) ceplane.plot(bcea_smoke, comparison = 2, wtp = 250) setComparisons(bcea_smoke) <- c(1,3) ceplane.plot(bcea_smoke, wtp = 250, graph = \"ggplot2\") eib.plot(bcea_smoke) contour(bcea_smoke) ceac.plot(bcea_smoke) ib.plot(bcea_smoke) #> NB: k (wtp) is defined in the interval [0 - 500] ceef.plot(bcea_smoke) #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.48486 94.919 195.77 1.5657 #> Individual counselling 0.72252 143.301 203.57 1.5659 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No intervention 0 0 Extended dominance bcea_smoke <- multi.ce(bcea_smoke) ceac.plot(bcea_smoke, pos = \"topright\") ceaf.plot(bcea_smoke) mixedAn(bcea_smoke) <- c(0.4, 0.3, 0.2, 0.1) summary(bcea_smoke, wtp = 250) #> #> Analysis of mixed strategy for willingness to pay parameter k = 250 #> #> Reference intervention: Group counselling (10.00% market share) #> Comparator intervention(s): No intervention (40.00% market share) #> : Individual counselling (20.00% market share) #> #> Loss in the expected value of information = 34.43 evi.plot(bcea_smoke, graph = \"ggplot\", pos = \"b\") r <- c(0, 0.005, 0.020, 0.035) CEriskav(bcea_smoke) <- r plot(bcea_smoke)"},{"path":"https://n8thangreen.github.io/BCEA/articles/R_journal_examples.html","id":"influenza-vaccine-data","dir":"Articles","previous_headings":"","what":"Influenza vaccine data","title":"R journal examples","text":"grid plots summary output risk aversion value information","code":"data(Vaccine) treats <- c(\"Status quo\", \"Vaccination\") bcea_vacc <- bcea(e, c, ref = 2, interventions = treats) plot(bcea_vacc) summary(bcea_vacc, wtp = 10000) #> #> Cost-effectiveness analysis summary #> #> Reference intervention: Vaccination #> Comparator intervention: Status quo #> #> Optimal decision: choose Status quo for k < 20100 and Vaccination for k >= 20100 #> #> #> Analysis for willingness to pay parameter k = 10000 #> #> Expected utility #> Status quo -36.054 #> Vaccination -34.826 #> #> EIB CEAC ICER #> Vaccination vs Status quo 1.2284 0.529 20098 #> #> Optimal intervention (max expected utility) for k = 10000: Status quo #> #> EVPI 3.0287 head(sim_table(bcea_vacc, wtp = 25000)$Table) #> U1 U2 U* IB2_1 OL VI #> 1 -36.57582 -38.71760 -36.57582 -2.1417866 2.141787 -1.135907 #> 2 -27.92514 -27.67448 -27.67448 0.2506573 0.000000 7.765431 #> 3 -28.03024 -33.37394 -28.03024 -5.3436963 5.343696 7.409665 #> 4 -53.28408 -47.13734 -47.13734 6.1467384 0.000000 -11.697432 #> 5 -43.58389 -40.40469 -40.40469 3.1791976 0.000000 -4.964782 #> 6 -42.37456 -33.08547 -33.08547 9.2890987 0.000000 2.354444 r <- c(0, 0.005, 0.020, 0.035) CEriskav(bcea_vacc) <- r plot(bcea_vacc) evi.plot(bcea_vacc) inp <- createInputs(vaccine_mat, print_is_linear_comb = FALSE) info.rank(bcea_vacc, inp) EVPPI <- evppi(bcea_vacc, c(\"beta.1.\", \"beta.2.\"), inp$mat) #> [1] \"method: GAM\" \"method: GAM\" #> #> Calculating fitted values for the GAM regression #> #> Calculating fitted values for the GAM regression #> Calculating EVPPI plot(EVPPI)"},{"path":"https://n8thangreen.github.io/BCEA/articles/Set_bcea_parameters.html","id":"changing-reference-group","dir":"Articles","previous_headings":"","what":"Changing Reference Group","title":"Set bcea() Parameters: Constructor and Setters","text":"Load cost-effectiveness data. first create bcea object using constructor function 2 different reference groups. Alternatively, can modifying first output.","code":"data(Vaccine) he_ref1 <- bcea(eff, cost, ref = 1, interventions = treats, Kmax = 50000) str(he_ref1) #> List of 24 #> $ n_sim : int 1000 #> $ n_comparators: num 2 #> $ n_comparisons: int 1 #> $ delta_e :'data.frame': 1000 obs. of 1 variable: #> ..$ Vaccination: num [1:1000] -0.000148 -0.000152 -0.000192 -0.000504 -0.000394 ... #> $ delta_c :'data.frame': 1000 obs. of 1 variable: #> ..$ Vaccination: num [1:1000] -5.84 -3.54 -10.15 -6.45 -6.68 ... #> $ ICER : Named num 20098 #> ..- attr(*, \"names\")= chr \"Vaccination\" #> $ Kmax : num 50000 #> $ k : num [1:501] 0 100 200 300 400 500 600 700 800 900 ... #> $ ceac : num [1:501, 1] 0.98 0.978 0.977 0.977 0.977 0.976 0.976 0.975 0.975 0.973 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ k : NULL #> .. ..$ ints: chr \"Vaccination\" #> $ ib : num [1:501, 1:1000, 1] 5.84 5.83 5.81 5.8 5.78 ... #> ..- attr(*, \"dimnames\")=List of 3 #> .. ..$ k : NULL #> .. ..$ sims: NULL #> .. ..$ ints: chr \"Vaccination\" #> $ eib : num [1:501, 1] 5.04 5.01 4.99 4.96 4.94 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ k : NULL #> .. ..$ ints: chr \"Vaccination\" #> $ kstar : num 20100 #> $ best : int [1:501] 1 1 1 1 1 1 1 1 1 1 ... #> $ U : num [1:1000, 1:501, 1:2] -10.41 -5.83 -5.78 -12.21 -9.79 ... #> ..- attr(*, \"dimnames\")=List of 3 #> .. ..$ sims: NULL #> .. ..$ k : NULL #> .. ..$ ints: chr [1:2] \"Status Quo\" \"Vaccination\" #> $ vi : num [1:1000, 1:501] -0.754 3.821 3.871 -2.553 -0.131 ... #> $ Ustar : num [1:1000, 1:501] -10.41 -5.83 -5.78 -12.21 -9.79 ... #> $ ol : num [1:1000, 1:501] 0 0 0 0 0 0 0 0 0 0 ... #> $ evi : num [1:501] 0.0562 0.0573 0.0586 0.0598 0.0611 ... #> $ ref : int 1 #> $ comp : int 2 #> $ step : num 100 #> $ interventions: chr [1:2] \"Status Quo\" \"Vaccination\" #> $ e : num [1:1000, 1:2] -0.001047 -0.000884 -0.00089 -0.001643 -0.001352 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ : NULL #> .. ..$ : chr [1:2] \"Status Quo\" \"Vaccination\" #> $ c : num [1:1000, 1:2] 10.41 5.83 5.78 12.21 9.79 ... #> ..- attr(*, \"dimnames\")=List of 2 #> .. ..$ : NULL #> .. ..$ : chr [1:2] \"Status Quo\" \"Vaccination\" #> - attr(*, \"class\")= chr [1:2] \"bcea\" \"list\" ceplane.plot(he_ref1) he_ref2 <- bcea(eff, cost, ref = 2, interventions = treats, Kmax = 50000) str(he_ref2[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #> $ n_comparators: num 2 #> $ ICER : Named num 20098 #> ..- attr(*, \"names\")= chr \"Status Quo\" #> $ ref : int 2 #> $ comp : int 1 setReferenceGroup(he_ref1) <- 2 str(he_ref1[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #> $ n_comparators: num 2 #> $ ICER : Named num 20098 #> ..- attr(*, \"names\")= chr \"Status Quo\" #> $ ref : int 2 #> $ comp : int 1"},{"path":"https://n8thangreen.github.io/BCEA/articles/Set_bcea_parameters.html","id":"changing-kmax","dir":"Articles","previous_headings":"","what":"Changing Kmax","title":"Set bcea() Parameters: Constructor and Setters","text":"way can change Kmax 2 equivalent ways.","code":"he_Kmax1 <- bcea(eff, cost, ref = 1, interventions = treats, Kmax = 50000) str(he_Kmax1[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\", \"Kmax\")]) #> List of 5 #> $ n_comparators: num 2 #> $ ICER : Named num 20098 #> ..- attr(*, \"names\")= chr \"Vaccination\" #> $ ref : int 1 #> $ comp : int 2 #> $ Kmax : num 50000 he_Kmax2 <- bcea(eff, cost, ref = 2, interventions = treats, Kmax = 2000) str(he_Kmax2[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\", \"Kmax\")]) #> List of 5 #> $ n_comparators: num 2 #> $ ICER : Named num 20098 #> ..- attr(*, \"names\")= chr \"Status Quo\" #> $ ref : int 2 #> $ comp : int 1 #> $ Kmax : num 2000 setKmax(he_Kmax1) <- 2000 str(he_Kmax1[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\", \"Kmax\")]) #> List of 5 #> $ n_comparators: num 2 #> $ ICER : Named num 20098 #> ..- attr(*, \"names\")= chr \"Vaccination\" #> $ ref : int 1 #> $ comp : int 2 #> $ Kmax : num 2000"},{"path":"https://n8thangreen.github.io/BCEA/articles/Set_bcea_parameters.html","id":"change-comparison-groups","dir":"Articles","previous_headings":"","what":"Change Comparison Groups","title":"Set bcea() Parameters: Constructor and Setters","text":"Lets load data two groups. Defaults groups case 2, 3 4. Let us compare groups 2. can achieve thing using appropriate setter. can select multiple comparison groups . Let us compare groups 2 4. , bcea object comparison groups can passed functions ceplane.plot ceac.plot comparison argument, modifications using functions internally instead.","code":"data(Smoking) he_comp234 <- bcea(eff, cost, ref = 1, interventions = treats, Kmax = 50000) str(he_comp234[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #> $ n_comparators: num 4 #> $ ICER : Named num [1:3] 159 196 198 #> ..- attr(*, \"names\")= chr [1:3] \"Self-help\" \"Individual counselling\" \"Group counselling\" #> $ ref : int 1 #> $ comp : int [1:3] 2 3 4 ceplane.plot(he_comp234, wtp = 2000) he_comp2 <- bcea(eff, cost, ref = 1, .comparison = 2, interventions = treats, Kmax = 2000) str(he_comp2[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #> $ n_comparators: num 2 #> $ ICER : Named num 159 #> ..- attr(*, \"names\")= chr \"Self-help\" #> $ ref : int 1 #> $ comp : num 2 ceplane.plot(he_comp2, wtp = 2000) setComparisons(he_comp234) <- 2 str(he_comp234[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #> $ n_comparators: num 2 #> $ ICER : Named num 159 #> ..- attr(*, \"names\")= chr \"Self-help\" #> $ ref : int 1 #> $ comp : num 2 ceplane.plot(he_comp234, wtp = 2000) he_comp24 <- bcea(eff, cost, ref = 1, .comparison = c(2,4), interventions = treats, Kmax = 2000) str(he_comp24[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #> $ n_comparators: num 3 #> $ ICER : Named num [1:2] 159 198 #> ..- attr(*, \"names\")= chr [1:2] \"Self-help\" \"Group counselling\" #> $ ref : int 1 #> $ comp : num [1:2] 2 4 ceplane.plot(he_comp24, wtp = 2000) setComparisons(he_comp234) <- c(2,4) str(he_comp234[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #> $ n_comparators: num 3 #> $ ICER : Named num [1:2] 159 198 #> ..- attr(*, \"names\")= chr [1:2] \"Self-help\" \"Group counselling\" #> $ ref : int 1 #> $ comp : num [1:2] 2 4 ceplane.plot(he_comp24, wtp = 2000) ceplane.plot(he_comp234, comparison = 2, wtp = 2000) ceplane.plot(he_comp234, comparison = c(2,4), wtp = 2000)"},{"path":"https://n8thangreen.github.io/BCEA/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Gianluca Baio. Author, maintainer, copyright holder. Andrea Berardi. Author. Anna Heath. Author. Nathan Green. Author.","code":""},{"path":"https://n8thangreen.github.io/BCEA/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Baio et al (2017). Bayesian Cost Effectiveness Analysis R package BCEA. Springer, New York, NY. doi: 10.1007/978-3-319-55718-2, URL: https://link.springer.com/book/10.1007/978-3-319-55718-2","code":"@Book{, title = {Bayesian Cost-Effectiveness Analysis with the R package {BCEA}}, author = {{Baio} and {G} and {Berardi} and {A} and {Heath} and {A}}, year = {2017}, publisher = {Springer}, month = {Jul}, address = {New York, NY}, url = {https://link.springer.com/book/10.1007/978-3-319-55718-2}, doi = {10.1007/978-3-319-55718-2}, isbn = {978-3-319-55718-2}, }"},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT_BACKUP_1725.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT_BASE_1725.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT_LOCAL_1725.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT_REMOTE_1725.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to BCEA","title":"Contributing to BCEA","text":"Thank taking time contribute development BCEA. find following guidelines useful making contributions. start: important valid GitHub account. Trivial changes comments documentation require creating new issue.","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"did-you-find-a-bug","dir":"","previous_headings":"","what":"Did you find a bug?","title":"Contributing to BCEA","text":"Make sure bug already reported Github Issues. Open issue clearly describe issue much information possible. code sample executable test case recommended.","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"did-you-plan-to-write-a-patch-that-fixes-a-bug","dir":"","previous_headings":"","what":"Did you plan to write a patch that fixes a bug?","title":"Contributing to BCEA","text":"Open issue clearly describe problem discuss solution affect BCEA. Fork repository GitHub work patch. Get touch maintainer refine prioritize issue.","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"making-changes-and-pull-requests","dir":"","previous_headings":"","what":"Making changes and Pull requests","title":"Contributing to BCEA","text":"Start work fork repository. haven’t done , try using usethis::create_from_github(\"n8thangreen/BCEA\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). Create Git branch pull request (PR). may want use usethis::pr_init(\"brief-description--change\"). Check unnecessary whitespace git diff --check format code. Commit messages descriptive, mentioning changed , also reference relevant issue number. Ensure add necessary tests changes (testthat preferably). Run tests assure nothing else accidentally broken, also keep eye test coverage metric. Commit git, create PR running usethis::pr_push(), following prompts browser","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"copyright-issues","dir":"","previous_headings":"","what":"Copyright issues","title":"Contributing to BCEA","text":"submission, crucial PR includes following statement: copyright code contributed, hereby grant BCEA repo cph unlimited license use code version future version BCEA. reserve rights code. may advisable contribute third party codes project. Useful suggestions nonetheless welcomed. PRs thereafter reviewed, feedback communicated soon possible.","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"additional-resources","dir":"","previous_headings":"","what":"Additional Resources","title":"Contributing to BCEA","text":"General GitHub documentation pull requests","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"bcea-bayesian-cost-effectiveness-analysis-","dir":"","previous_headings":"","what":"Bayesian Cost Effectiveness Analysis","title":"Bayesian Cost Effectiveness Analysis","text":"Perform Bayesian Cost-Effectiveness Analysis R. 🚀 Version 2.4.5 development now! Check release notes .","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"contents","dir":"","previous_headings":"","what":"Contents","title":"Bayesian Cost Effectiveness Analysis","text":"Overview Features Installation Articles details","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Bayesian Cost Effectiveness Analysis","text":"Given results Bayesian model (possibly based MCMC) form simulations posterior distributions suitable variables costs clinical benefits two interventions, produces health economic evaluation. Compares one interventions (“reference”) others (“comparators”).","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"Bayesian Cost Effectiveness Analysis","text":"Main features BCEA include: Cost-effectiveness analysis plots, CE planes CEAC Summary statistics tables EVPPI calculations plots","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Bayesian Cost Effectiveness Analysis","text":"Install released version CRAN stable version (can updated quickly) can installed using GitHub repository. Windows machines, need install dependencies, including Rtools first, e.g. running installing package using remotes: Linux MacOS, sufficient install package via remotes:","code":"install.packages(\"BCEA\") pkgs <- c(\"MASS\", \"Rtools\", \"remotes\") repos <- c(\"https://cran.rstudio.com\", \"https://inla.r-inla-download.org/R/stable/\") install.packages(pkgs, repos=repos, dependencies = \"Depends\") remotes::install_github(\"giabaio/BCEA\") install.packages(\"remotes\") remotes::install_github(\"giabaio/BCEA\")"},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"articles","dir":"","previous_headings":"","what":"Articles","title":"Bayesian Cost Effectiveness Analysis","text":"Examples using specific functions different arguments given articles: Get Started Set bcea() Parameters: Constructor Setters Cost-Effectiveness Acceptability Curve Plots Cost-Effectiveness Efficiency Frontier Risk Aversion Analysis Expected Incremental Benefit Plot Paired vs Multiple Comparisons","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"further-details","dir":"","previous_headings":"","what":"Further details","title":"Bayesian Cost Effectiveness Analysis","text":"pkgdown site . details BCEA available book Bayesian Cost-Effectiveness Analysis R Package BCEA (published UseR! Springer series). Also, details package, including references links pdf presentation posts blog) given .","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Bayesian Cost Effectiveness Analysis","text":"Please submit contributions Pull Requests, following contributing guidelines. report issues /seek support, please file new ticket issue tracker. Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc. Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. General Public Licenses designed make sure freedom distribute copies free software (charge wish), receive source code can get want , can change software use pieces new free programs, know can things. protect rights, need prevent others denying rights asking surrender rights. Therefore, certain responsibilities distribute copies software, modify : responsibilities respect freedom others. example, distribute copies program, whether gratis fee, must pass recipients freedoms received. must make sure , , receive can get source code. must show terms know rights. Developers use GNU GPL protect rights two steps: (1) assert copyright software, (2) offer License giving legal permission copy, distribute /modify . developers’ authors’ protection, GPL clearly explains warranty free software. users’ authors’ sake, GPL requires modified versions marked changed, problems attributed erroneously authors previous versions. devices designed deny users access install run modified versions software inside , although manufacturer can . fundamentally incompatible aim protecting users’ freedom change software. systematic pattern abuse occurs area products individuals use, precisely unacceptable. Therefore, designed version GPL prohibit practice products. problems arise substantially domains, stand ready extend provision domains future versions GPL, needed protect freedom users. Finally, every program threatened constantly software patents. States allow patents restrict development use software general-purpose computers, , wish avoid special danger patents applied free program make effectively proprietary. prevent , GPL assures patents used render program non-free. precise terms conditions copying, distribution modification follow.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_0-definitions","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"0. Definitions","title":"GNU General Public License","text":"“License” refers version 3 GNU General Public License. “Copyright” also means copyright-like laws apply kinds works, semiconductor masks. “Program” refers copyrightable work licensed License. licensee addressed “”. “Licensees” “recipients” may individuals organizations. “modify” work means copy adapt part work fashion requiring copyright permission, making exact copy. resulting work called “modified version” earlier work work “based ” earlier work. “covered work” means either unmodified Program work based Program. “propagate” work means anything , without permission, make directly secondarily liable infringement applicable copyright law, except executing computer modifying private copy. Propagation includes copying, distribution (without modification), making available public, countries activities well. “convey” work means kind propagation enables parties make receive copies. Mere interaction user computer network, transfer copy, conveying. interactive user interface displays “Appropriate Legal Notices” extent includes convenient prominently visible feature (1) displays appropriate copyright notice, (2) tells user warranty work (except extent warranties provided), licensees may convey work License, view copy License. interface presents list user commands options, menu, prominent item list meets criterion.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_1-source-code","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"1. Source Code","title":"GNU General Public License","text":"“source code” work means preferred form work making modifications . “Object code” means non-source form work. “Standard Interface” means interface either official standard defined recognized standards body, , case interfaces specified particular programming language, one widely used among developers working language. “System Libraries” executable work include anything, work whole, () included normal form packaging Major Component, part Major Component, (b) serves enable use work Major Component, implement Standard Interface implementation available public source code form. “Major Component”, context, means major essential component (kernel, window system, ) specific operating system () executable work runs, compiler used produce work, object code interpreter used run . “Corresponding Source” work object code form means source code needed generate, install, (executable work) run object code modify work, including scripts control activities. However, include work’s System Libraries, general-purpose tools generally available free programs used unmodified performing activities part work. example, Corresponding Source includes interface definition files associated source files work, source code shared libraries dynamically linked subprograms work specifically designed require, intimate data communication control flow subprograms parts work. Corresponding Source need include anything users can regenerate automatically parts Corresponding Source. Corresponding Source work source code form work.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_2-basic-permissions","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"2. Basic Permissions","title":"GNU General Public License","text":"rights granted License granted term copyright Program, irrevocable provided stated conditions met. License explicitly affirms unlimited permission run unmodified Program. output running covered work covered License output, given content, constitutes covered work. License acknowledges rights fair use equivalent, provided copyright law. may make, run propagate covered works convey, without conditions long license otherwise remains force. may convey covered works others sole purpose make modifications exclusively , provide facilities running works, provided comply terms License conveying material control copyright. thus making running covered works must exclusively behalf, direction control, terms prohibit making copies copyrighted material outside relationship . Conveying circumstances permitted solely conditions stated . Sublicensing allowed; section 10 makes unnecessary.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_3-protecting-users-legal-rights-from-anti-circumvention-law","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"3. Protecting Users’ Legal Rights From Anti-Circumvention Law","title":"GNU General Public License","text":"covered work shall deemed part effective technological measure applicable law fulfilling obligations article 11 WIPO copyright treaty adopted 20 December 1996, similar laws prohibiting restricting circumvention measures. convey covered work, waive legal power forbid circumvention technological measures extent circumvention effected exercising rights License respect covered work, disclaim intention limit operation modification work means enforcing, work’s users, third parties’ legal rights forbid circumvention technological measures.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_4-conveying-verbatim-copies","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"4. Conveying Verbatim Copies","title":"GNU General Public License","text":"may convey verbatim copies Program’s source code receive , medium, provided conspicuously appropriately publish copy appropriate copyright notice; keep intact notices stating License non-permissive terms added accord section 7 apply code; keep intact notices absence warranty; give recipients copy License along Program. may charge price price copy convey, may offer support warranty protection fee.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_5-conveying-modified-source-versions","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"5. Conveying Modified Source Versions","title":"GNU General Public License","text":"may convey work based Program, modifications produce Program, form source code terms section 4, provided also meet conditions: ) work must carry prominent notices stating modified , giving relevant date. b) work must carry prominent notices stating released License conditions added section 7. requirement modifies requirement section 4 “keep intact notices”. c) must license entire work, whole, License anyone comes possession copy. License therefore apply, along applicable section 7 additional terms, whole work, parts, regardless packaged. License gives permission license work way, invalidate permission separately received . d) work interactive user interfaces, must display Appropriate Legal Notices; however, Program interactive interfaces display Appropriate Legal Notices, work need make . compilation covered work separate independent works, nature extensions covered work, combined form larger program, volume storage distribution medium, called “aggregate” compilation resulting copyright used limit access legal rights compilation’s users beyond individual works permit. Inclusion covered work aggregate cause License apply parts aggregate.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_6-conveying-non-source-forms","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"6. Conveying Non-Source Forms","title":"GNU General Public License","text":"may convey covered work object code form terms sections 4 5, provided also convey machine-readable Corresponding Source terms License, one ways: ) Convey object code , embodied , physical product (including physical distribution medium), accompanied Corresponding Source fixed durable physical medium customarily used software interchange. b) Convey object code , embodied , physical product (including physical distribution medium), accompanied written offer, valid least three years valid long offer spare parts customer support product model, give anyone possesses object code either (1) copy Corresponding Source software product covered License, durable physical medium customarily used software interchange, price reasonable cost physically performing conveying source, (2) access copy Corresponding Source network server charge. c) Convey individual copies object code copy written offer provide Corresponding Source. alternative allowed occasionally noncommercially, received object code offer, accord subsection 6b. d) Convey object code offering access designated place (gratis charge), offer equivalent access Corresponding Source way place charge. need require recipients copy Corresponding Source along object code. place copy object code network server, Corresponding Source may different server (operated third party) supports equivalent copying facilities, provided maintain clear directions next object code saying find Corresponding Source. Regardless server hosts Corresponding Source, remain obligated ensure available long needed satisfy requirements. e) Convey object code using peer--peer transmission, provided inform peers object code Corresponding Source work offered general public charge subsection 6d. separable portion object code, whose source code excluded Corresponding Source System Library, need included conveying object code work. “User Product” either (1) “consumer product”, means tangible personal property normally used personal, family, household purposes, (2) anything designed sold incorporation dwelling. determining whether product consumer product, doubtful cases shall resolved favor coverage. particular product received particular user, “normally used” refers typical common use class product, regardless status particular user way particular user actually uses, expects expected use, product. product consumer product regardless whether product substantial commercial, industrial non-consumer uses, unless uses represent significant mode use product. “Installation Information” User Product means methods, procedures, authorization keys, information required install execute modified versions covered work User Product modified version Corresponding Source. information must suffice ensure continued functioning modified object code case prevented interfered solely modification made. convey object code work section , , specifically use , User Product, conveying occurs part transaction right possession use User Product transferred recipient perpetuity fixed term (regardless transaction characterized), Corresponding Source conveyed section must accompanied Installation Information. requirement apply neither third party retains ability install modified object code User Product (example, work installed ROM). requirement provide Installation Information include requirement continue provide support service, warranty, updates work modified installed recipient, User Product modified installed. Access network may denied modification materially adversely affects operation network violates rules protocols communication across network. Corresponding Source conveyed, Installation Information provided, accord section must format publicly documented (implementation available public source code form), must require special password key unpacking, reading copying.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_7-additional-terms","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"7. Additional Terms","title":"GNU General Public License","text":"“Additional permissions” terms supplement terms License making exceptions one conditions. Additional permissions applicable entire Program shall treated though included License, extent valid applicable law. additional permissions apply part Program, part may used separately permissions, entire Program remains governed License without regard additional permissions. convey copy covered work, may option remove additional permissions copy, part . (Additional permissions may written require removal certain cases modify work.) may place additional permissions material, added covered work, can give appropriate copyright permission. Notwithstanding provision License, material add covered work, may (authorized copyright holders material) supplement terms License terms: ) Disclaiming warranty limiting liability differently terms sections 15 16 License; b) Requiring preservation specified reasonable legal notices author attributions material Appropriate Legal Notices displayed works containing ; c) Prohibiting misrepresentation origin material, requiring modified versions material marked reasonable ways different original version; d) Limiting use publicity purposes names licensors authors material; e) Declining grant rights trademark law use trade names, trademarks, service marks; f) Requiring indemnification licensors authors material anyone conveys material (modified versions ) contractual assumptions liability recipient, liability contractual assumptions directly impose licensors authors. non-permissive additional terms considered “restrictions” within meaning section 10. Program received , part , contains notice stating governed License along term restriction, may remove term. license document contains restriction permits relicensing conveying License, may add covered work material governed terms license document, provided restriction survive relicensing conveying. add terms covered work accord section, must place, relevant source files, statement additional terms apply files, notice indicating find applicable terms. Additional terms, permissive non-permissive, may stated form separately written license, stated exceptions; requirements apply either way.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_8-termination","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"8. Termination","title":"GNU General Public License","text":"may propagate modify covered work except expressly provided License. attempt otherwise propagate modify void, automatically terminate rights License (including patent licenses granted third paragraph section 11). However, cease violation License, license particular copyright holder reinstated () provisionally, unless copyright holder explicitly finally terminates license, (b) permanently, copyright holder fails notify violation reasonable means prior 60 days cessation. Moreover, license particular copyright holder reinstated permanently copyright holder notifies violation reasonable means, first time received notice violation License (work) copyright holder, cure violation prior 30 days receipt notice. Termination rights section terminate licenses parties received copies rights License. rights terminated permanently reinstated, qualify receive new licenses material section 10.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_9-acceptance-not-required-for-having-copies","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"9. Acceptance Not Required for Having Copies","title":"GNU General Public License","text":"required accept License order receive run copy Program. Ancillary propagation covered work occurring solely consequence using peer--peer transmission receive copy likewise require acceptance. However, nothing License grants permission propagate modify covered work. actions infringe copyright accept License. Therefore, modifying propagating covered work, indicate acceptance License .","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_10-automatic-licensing-of-downstream-recipients","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"10. Automatic Licensing of Downstream Recipients","title":"GNU General Public License","text":"time convey covered work, recipient automatically receives license original licensors, run, modify propagate work, subject License. responsible enforcing compliance third parties License. “entity transaction” transaction transferring control organization, substantially assets one, subdividing organization, merging organizations. propagation covered work results entity transaction, party transaction receives copy work also receives whatever licenses work party’s predecessor interest give previous paragraph, plus right possession Corresponding Source work predecessor interest, predecessor can get reasonable efforts. may impose restrictions exercise rights granted affirmed License. example, may impose license fee, royalty, charge exercise rights granted License, may initiate litigation (including cross-claim counterclaim lawsuit) alleging patent claim infringed making, using, selling, offering sale, importing Program portion .","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_11-patents","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"11. Patents","title":"GNU General Public License","text":"“contributor” copyright holder authorizes use License Program work Program based. work thus licensed called contributor’s “contributor version”. contributor’s “essential patent claims” patent claims owned controlled contributor, whether already acquired hereafter acquired, infringed manner, permitted License, making, using, selling contributor version, include claims infringed consequence modification contributor version. purposes definition, “control” includes right grant patent sublicenses manner consistent requirements License. contributor grants non-exclusive, worldwide, royalty-free patent license contributor’s essential patent claims, make, use, sell, offer sale, import otherwise run, modify propagate contents contributor version. following three paragraphs, “patent license” express agreement commitment, however denominated, enforce patent (express permission practice patent covenant sue patent infringement). “grant” patent license party means make agreement commitment enforce patent party. convey covered work, knowingly relying patent license, Corresponding Source work available anyone copy, free charge terms License, publicly available network server readily accessible means, must either (1) cause Corresponding Source available, (2) arrange deprive benefit patent license particular work, (3) arrange, manner consistent requirements License, extend patent license downstream recipients. “Knowingly relying” means actual knowledge , patent license, conveying covered work country, recipient’s use covered work country, infringe one identifiable patents country reason believe valid. , pursuant connection single transaction arrangement, convey, propagate procuring conveyance , covered work, grant patent license parties receiving covered work authorizing use, propagate, modify convey specific copy covered work, patent license grant automatically extended recipients covered work works based . patent license “discriminatory” include within scope coverage, prohibits exercise , conditioned non-exercise one rights specifically granted License. may convey covered work party arrangement third party business distributing software, make payment third party based extent activity conveying work, third party grants, parties receive covered work , discriminatory patent license () connection copies covered work conveyed (copies made copies), (b) primarily connection specific products compilations contain covered work, unless entered arrangement, patent license granted, prior 28 March 2007. Nothing License shall construed excluding limiting implied license defenses infringement may otherwise available applicable patent law.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_12-no-surrender-of-others-freedom","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"12. No Surrender of Others’ Freedom","title":"GNU General Public License","text":"conditions imposed (whether court order, agreement otherwise) contradict conditions License, excuse conditions License. convey covered work satisfy simultaneously obligations License pertinent obligations, consequence may convey . example, agree terms obligate collect royalty conveying convey Program, way satisfy terms License refrain entirely conveying Program.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_13-use-with-the-gnu-affero-general-public-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"13. Use with the GNU Affero General Public License","title":"GNU General Public License","text":"Notwithstanding provision License, permission link combine covered work work licensed version 3 GNU Affero General Public License single combined work, convey resulting work. terms License continue apply part covered work, special requirements GNU Affero General Public License, section 13, concerning interaction network apply combination .","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_14-revised-versions-of-this-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"14. Revised Versions of this License","title":"GNU General Public License","text":"Free Software Foundation may publish revised /new versions GNU General Public License time time. new versions similar spirit present version, may differ detail address new problems concerns. version given distinguishing version number. Program specifies certain numbered version GNU General Public License “later version” applies , option following terms conditions either numbered version later version published Free Software Foundation. Program specify version number GNU General Public License, may choose version ever published Free Software Foundation. Program specifies proxy can decide future versions GNU General Public License can used, proxy’s public statement acceptance version permanently authorizes choose version Program. Later license versions may give additional different permissions. However, additional obligations imposed author copyright holder result choosing follow later version.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_15-disclaimer-of-warranty","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"15. Disclaimer of Warranty","title":"GNU General Public License","text":"WARRANTY PROGRAM, EXTENT PERMITTED APPLICABLE LAW. EXCEPT OTHERWISE STATED WRITING COPYRIGHT HOLDERS /PARTIES PROVIDE PROGRAM “” WITHOUT WARRANTY KIND, EITHER EXPRESSED IMPLIED, INCLUDING, LIMITED , IMPLIED WARRANTIES MERCHANTABILITY FITNESS PARTICULAR PURPOSE. ENTIRE RISK QUALITY PERFORMANCE PROGRAM . PROGRAM PROVE DEFECTIVE, ASSUME COST NECESSARY SERVICING, REPAIR CORRECTION.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_16-limitation-of-liability","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"16. Limitation of Liability","title":"GNU General Public License","text":"EVENT UNLESS REQUIRED APPLICABLE LAW AGREED WRITING COPYRIGHT HOLDER, PARTY MODIFIES /CONVEYS PROGRAM PERMITTED , LIABLE DAMAGES, INCLUDING GENERAL, SPECIAL, INCIDENTAL CONSEQUENTIAL DAMAGES ARISING USE INABILITY USE PROGRAM (INCLUDING LIMITED LOSS DATA DATA RENDERED INACCURATE LOSSES SUSTAINED THIRD PARTIES FAILURE PROGRAM OPERATE PROGRAMS), EVEN HOLDER PARTY ADVISED POSSIBILITY DAMAGES.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_17-interpretation-of-sections-15-and-16","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"17. Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see . GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read .","code":" Copyright (C) 2020 G Baio This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . BCEA Copyright (C) 2020 G Baio This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"contents","dir":"","previous_headings":"","what":"Contents","title":"BCEA — development version ","text":"Overview Features Installation details","code":""},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"BCEA — development version ","text":"Perform Bayesian Cost-Effectiveness Analysis R. Given results Bayesian model (possibly based MCMC) form simulations posterior distributions suitable variables costs clinical benefits two interventions, produces health economic evaluation. Compares one interventions (“reference”) others (“comparators”).","code":""},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"BCEA — development version ","text":"Main features BCEA include: Summary statistics tables Cost-effectiveness analysis plots, CE planes CEAC EVPPI calculations plots development version BCEA (currently 2.4). contains major refactoring code streamline functions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"BCEA — development version ","text":"development version can installed using GitHub repository. Windows machines, need install dependencies, including Rtools first, e.g. running installing package using remotes: Linux MacOS, sufficient install package via remotes:","code":"pkgs <- c(\"MASS\", \"Rtools\", \"remotes\") repos <- c(\"https://cran.rstudio.com\", \"https://inla.r-inla-download.org/R/stable/\") install.packages(pkgs, repos=repos, dependencies = \"Depends\") remotes::install_github(\"giabaio/BCEA\", ref=\"dev\") install.packages(\"remotes\") remotes::install_github(\"giabaio/BCEA\", ref=\"dev\")"},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"further-details","dir":"","previous_headings":"","what":"Further details","title":"BCEA — development version ","text":"pkgdown site . details BCEA available book Bayesian Cost-Effectiveness Analysis R Package BCEA (published UseR! Springer series). Also, details package, including references links pdf presentation posts blog) given .","code":""},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"licence","dir":"","previous_headings":"","what":"Licence","title":"BCEA — development version ","text":"GPL-3 © G Baio. Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contours.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Contours to Base R Plot — add_contours","title":"Add Contours to Base R Plot — add_contours","text":"Add Contours Base R Plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contours.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Contours to Base R Plot — add_contours","text":"","code":"add_contours(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contours.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Contours to Base R Plot — add_contours","text":"bcea object containing results Bayesian modelling economic evaluation. params List","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contours.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Contours to Base R Plot — add_contours","text":"plot side effect","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contour_quadrants.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Contour Quadrants — add_contour_quadrants","title":"Add Contour Quadrants — add_contour_quadrants","text":"Add Contour Quadrants","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contour_quadrants.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Contour Quadrants — add_contour_quadrants","text":"","code":"add_contour_quadrants(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contour_quadrants.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Contour Quadrants — add_contour_quadrants","text":"bcea object containing results Bayesian modelling economic evaluation. params List","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contour_quadrants.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Contour Quadrants — add_contour_quadrants","text":"Plot side effect","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated functions in package BCEA. — BCEA-deprecated","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"functions listed deprecated defunct near future. possible, alternative functions similar functionality also mentioned. Help pages deprecated functions available help(\"-deprecated\").","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"","code":"plot.mixedAn(x, y.limits=NULL, pos=c(0,1), graph=c(\"base\",\"ggplot2\"),...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"x object class mixedAn, given output call function mixedAn(). y.limits Range y-axis graph. default value NULL, case maximum range optimal mixed analysis scenarios considered. pos Parameter set position legend. Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. Default value c(0,1), topleft corner inside plot area. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods, graphical parameters (see par()).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"evi ggplot object containing plot. Returned graph=\"ggplot2\". function produces graph showing difference ''optimal'' version EVPI (cost-effective intervention included market) mixed strategy one (one intervention considered market).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"plot-mixedan","dir":"Reference","previous_headings":"","what":"plot.mixedAn","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"plot.mixedAn, use evi.plot(). Summary plot health economic analysis mixed analysis considered Compares optimal scenario mixed case terms EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Deprecated functions in package BCEA. — BCEA-deprecated","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-package.html","id":null,"dir":"Reference","previous_headings":"","what":"BCEA: Bayesian Cost Effectiveness Analysis — BCEA-package","title":"BCEA: Bayesian Cost Effectiveness Analysis — BCEA-package","text":"Produces economic evaluation sample suitable variables cost effectiveness / utility two interventions, e.g. Bayesian model form MCMC simulations. package computes cost-effective alternative produces graphical summaries probabilistic sensitivity analysis, see Baio et al (2017) doi:10.1007/978-3-319-55718-2 .","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BCEA: Bayesian Cost Effectiveness Analysis — BCEA-package","text":"Maintainer: Gianluca Baio g.baio@ucl.ac.uk (ORCID) [copyright holder] Authors: Andrea Berardi .berardi@ucl.ac.uk (ORCID) Anna Heath anna.heath@sickkids.ca (ORCID) Nathan Green n.green@ucl.ac.uk (ORCID)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"Cost-effectiveness analysis based results simulation model variable clinical benefits (e) costs (c). Produces results post-processed give health economic analysis. output stored object class \"bcea\".","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"","code":"bcea( eff, cost, ref = 1, interventions = NULL, .comparison = NULL, Kmax = 50000, k = NULL, plot = FALSE, ... ) # S3 method for default bcea( eff, cost, ref = NULL, interventions = NULL, .comparison = NULL, Kmax = 50000, k = NULL, plot = FALSE, ... ) # S3 method for rjags bcea(eff, ...) # S3 method for rstan bcea(eff, ...) # S3 method for bugs bcea(eff, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"eff object containing nsim simulations variable clinical effectiveness intervention considered. general matrix nsim rows nint columns. partially matched e' previous version BCEA` back-compatibility. cost object containing nsim simulations variable cost intervention considered. general matrix nsim rows nint columns. partially matched c' previous version BCEA` back-compatibility. ref Defines intervention (columns eff cost) considered reference strategy. default value ref = 1 means intervention associated first column eff cost reference one(s) associated column(s) () comparators. interventions Defines labels associated intervention. default NULL, assigns labels form \"Intervention1\", ... , \"InterventionT\". .comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison = 2). Kmax Maximum value willingness pay considered. Default value k = 50000. willingness pay approximated discrete grid interval [0, Kmax]. grid equal k parameter given, composed 501 elements k = NULL (default). k (n optional) vector values willingness pay grid. length > 1 otherwise plots empty. specified BCEA construct grid 501 values 0 Kmax. option useful performing intensive computations (e.g. EVPPI). changed wtp previous versions consistency functions deprecated future. plot logical value indicating whether function produce summary plot . ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"object class \"bcea\" containing following elements n_sim Number simulations produced Bayesian model n_comparators Number interventions analysed n_comparisons Number possible pairwise comparisons delta.e possible comparison, differential effectiveness measure delta.c possible comparison, differential cost measure ICER value Incremental Cost-Effectiveness Ratio Kmax maximum value assumed willingness pay threshold k vector values grid approximation willingness pay ceac value Cost-Effectiveness Acceptability Curve, function willingness pay ib distribution Incremental Benefit, given willingness pay eib value Expected Incremental Benefit, function willingness pay kstar grid approximation break-even point(s) best vector containing numeric label intervention cost-effective value willingness pay selected grid approximation U array including value expected utility simulation Bayesian model, value grid approximation willingness pay intervention considered vi array including value information simulation Bayesian model value grid approximation willingness pay Ustar array including maximum \"known-distribution\" utility simulation Bayesian model value grid approximation willingness pay ol array including opportunity loss simulation Bayesian model value grid approximation willingness pay evi vector values Expected Value Information, function willingness pay interventions vector labels interventions considered ref numeric index associated intervention used reference analysis comp numeric index(es) associated intervention(s) used comparator(s) analysis step step size used form grid approximation willingness pay e eff matrix used generate object (see Arguments) c cost matrix used generate object (see Arguments)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"Baio G (2013). Bayesian Methods Health Economics. CRC. Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"Gianluca Baio, Andrea Berardi, Nathan Green","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create Bayesian Cost-Effectiveness Analysis Object — bcea","text":"","code":"# See Baio (2013), Baio (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea( e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) plot=TRUE # plots the results ) # Creates a summary table summary( m, # uses the results of the economic evaluation # (a \"bcea\" object) wtp=25000 # selects the particular value for k ) #> #> Cost-effectiveness analysis summary #> #> Reference intervention: Vaccination #> Comparator intervention: Status Quo #> #> Optimal decision: choose Status Quo for k < 20100 and Vaccination for k >= 20100 #> #> #> Analysis for willingness to pay parameter k = 25000 #> #> Expected net benefit #> Status Quo -36.054 #> Vaccination -34.826 #> #> EIB CEAC ICER #> Vaccination vs Status Quo 1.2284 0.529 20098 #> #> Optimal intervention (max expected net benefit) for k = 25000: Vaccination #> #> EVPI 2.4145 # \\donttest{ # Plots the cost-effectiveness plane using base graphics ceplane.plot( m, # plots the Cost-Effectiveness plane comparison=1, # if more than 2 interventions, selects the # pairwise comparison wtp=25000, # selects the relevant willingness to pay # (default: 25,000) graph=\"base\" # selects base graphics (default) ) # Plots the cost-effectiveness plane using ggplot2 if (requireNamespace(\"ggplot2\")) { ceplane.plot( m, # plots the Cost-Effectiveness plane comparison=1, # if more than 2 interventions, selects the # pairwise comparison wtp=25000, # selects the relevant willingness to pay # (default: 25,000) graph=\"ggplot2\"# selects ggplot2 as the graphical engine ) # Some more options ceplane.plot( m, graph=\"ggplot2\", pos=\"top\", size=5, ICER_size=1.5, label.pos=FALSE, opt.theme=ggplot2::theme(text=ggplot2::element_text(size=8)) ) } # Plots the contour and scatterplot of the bivariate # distribution of (Delta_e,Delta_c) contour( m, # uses the results of the economic evaluation # (a \"bcea\" object) comparison=1, # if more than 2 interventions, selects the # pairwise comparison nlevels=4, # selects the number of levels to be # plotted (default=4) levels=NULL, # specifies the actual levels to be plotted # (default=NULL, so that R will decide) scale=0.5, # scales the bandwidths for both x- and # y-axis (default=0.5) graph=\"base\" # uses base graphics to produce the plot ) # Plots the contour and scatterplot of the bivariate # distribution of (Delta_e,Delta_c) contour2( m, # uses the results of the economic evaluation # (a \"bcea\" object) wtp=25000, # selects the willingness-to-pay threshold ) # Using ggplot2 if (requireNamespace(\"ggplot2\")) { contour2( m, # uses the results of the economic evaluation # (a \"bcea\" object) graph=\"ggplot2\",# selects the graphical engine wtp=25000, # selects the willingness-to-pay threshold label.pos=FALSE # alternative position for the wtp label ) } # Plots the Expected Incremental Benefit for the \"bcea\" object m eib.plot(m) # Plots the distribution of the Incremental Benefit ib.plot( m, # uses the results of the economic evaluation # (a \"bcea\" object) comparison=1, # if more than 2 interventions, selects the # pairwise comparison wtp=25000, # selects the relevant willingness # to pay (default: 25,000) graph=\"base\" # uses base graphics ) # Produces a plot of the CEAC against a grid of values for the # willingness to pay threshold ceac.plot(m) # Plots the Expected Value of Information for the \"bcea\" object m evi.plot(m) # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimal intervention — best_interv_given_k","title":"Optimal intervention — best_interv_given_k","text":"Select best option value willingness pay.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimal intervention — best_interv_given_k","text":"","code":"best_interv_given_k(eib, ref, comp)"},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimal intervention — best_interv_given_k","text":"eib Expected incremental benefit ref Reference group number comp Comparison group number(s)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimal intervention — best_interv_given_k","text":"Group index","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"Produces plot Cost-Effectiveness Acceptability Curve (CEAC) willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"","code":"# S3 method for bcea ceac.plot( he, comparison = NULL, pos = c(1, 0), graph = c(\"base\", \"ggplot2\", \"plotly\"), ... ) ceac.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". plotting functions \"plotly\" implementation yet. ... graph = \"ggplot2\" named theme object supplied, passed ggplot2 object. usual ggplot2 syntax used. Additional arguments: line = list(color): specifies line colour(s) - graph types. line = list(type): specifies line type(s) lty numeric values - graph types. line = list(size): specifies line width(s) numeric values - graph types. currency: Currency prefix willingness pay values - ggplot2 . area_include: logical, include area CEAC curves - plotly . area_color: specifies AUC colour - plotly .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"ceac graph = \"ggplot2\" ggplot object, graph = \"plotly\" plotly object containing requested plot. Nothing returned graph = \"base\", default. function produces plot cost-effectiveness acceptability curve discrete grid possible values willingness pay parameter. Values CEAC closer 1 indicate uncertainty cost-effectiveness reference intervention low. Similarly, values CEAC closer 0 indicate uncertainty cost-effectiveness comparator low.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"CEAC estimates probability cost-effectiveness, respect given willingness pay threshold. CEAC used mainly evaluate uncertainty associated decision-making process, since enables quantification preference compared interventions, defined terms difference utilities. Formally, CEAC defined : $$\\textrm{CEAC} = P(\\textrm{IB}(\\theta) > 0)$$ net benefit function used utility function, definition can re-written $$\\textrm{CEAC} = P(k \\cdot \\Delta_e - \\Delta_c > 0)$$ effectively depending willingness pay value \\(k\\).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot — ceac.plot.bcea","text":"","code":"data(\"Vaccine\") he <- BCEA::bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ceac.plot(he) ceac.plot(he, graph = \"base\") ceac.plot(he, graph = \"ggplot2\") ceac.plot(he, graph = \"plotly\") {\"x\":{\"visdat\":{\"4cac41787eda\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"4cac41787eda\",\"attrs\":{\"4cac41787eda\":{\"x\":{},\"alpha_stroke\":1,\"sizes\":[10,100],\"spans\":[1,20],\"y\":{},\"type\":\"scatter\",\"mode\":\"lines\",\"fill\":\"none\",\"name\":{},\"color\":{},\"colors\":\"black\",\"linetype\":{},\"linetypes\":1,\"inherit\":true}},\"layout\":{\"margin\":{\"b\":40,\"l\":60,\"t\":25,\"r\":10},\"title\":\"Cost Effectiveness Acceptability Curve\",\"xaxis\":{\"domain\":[0,1],\"automargin\":true,\"hoverformat\":\".2f\",\"title\":\"Willingness to pay\"},\"yaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"Probability of cost 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2\",\"line\":{\"color\":\"rgba(0,0,0,1)\",\"dash\":\"solid\"},\"marker\":{\"color\":\"rgba(0,0,0,1)\",\"line\":{\"color\":\"rgba(0,0,0,1)\"}},\"textfont\":{\"color\":\"rgba(0,0,0,1)\"},\"error_y\":{\"color\":\"rgba(0,0,0,1)\"},\"error_x\":{\"color\":\"rgba(0,0,0,1)\"},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null}],\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} ceac.plot(he, graph = \"ggplot2\", title = \"my title\", line = list(color = \"green\"), theme = ggplot2::theme_dark()) ## more interventions he2 <- BCEA::bcea(cbind(eff, eff - 0.0002), cbind(cost, cost + 5)) #> No reference selected. 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ceac_plot_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot By Graph Device — ceac_plot_graph","text":"","code":"ceac_plot_base(he, pos_legend, graph_params, ...) # S3 method for pairwise ceac_plot_base(he, pos_legend, graph_params, ...) # S3 method for bcea ceac_plot_base(he, pos_legend, graph_params, ...) ceac_plot_ggplot(he, pos_legend, graph_params, ...) # S3 method for pairwise ceac_plot_ggplot(he, pos_legend, graph_params, ...) # S3 method for bcea ceac_plot_ggplot(he, pos_legend, graph_params, ...) ceac_ggplot(he, pos_legend, graph_params, ceac, ...) ceac_plot_plotly(he, pos_legend = \"left\", graph_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot By Graph Device — ceac_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. pos_legend Legend position graph_params Aesthetic ggplot parameters ... Additional arguments ceac ceac index ","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"Produces plot Cost-Effectiveness Acceptability Frontier (CEAF) willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"","code":"# S3 method for pairwise ceaf.plot(mce, graph = c(\"base\", \"ggplot2\"), ...) ceaf.plot(mce, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"mce output call function multi.ce() graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"ceaf ggplot object containing plot. Returned graph=\"ggplot2\".","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot — ceaf.plot.pairwise","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea( e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) plot=FALSE # inhibits graphical output ) # \\donttest{ mce <- multi.ce(m) # uses the results of the economic analysis # } # \\donttest{ ceaf.plot(mce) # plots the CEAF # } # \\donttest{ ceaf.plot(mce, graph = \"g\") # uses ggplot2 # } # \\donttest{ # Use the smoking cessation dataset data(Smoking) m <- bcea(eff, cost, ref = 4, intervention = treats, Kmax = 500, plot = FALSE) mce <- multi.ce(m) ceaf.plot(mce) # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"line connecting successive points cost-effectiveness plane represent effect cost associated different treatment alternatives. gradient line segment represents ICER treatment comparison two alternatives represented segment. cost-effectiveness frontier consists set points corresponding treatment alternatives considered cost-effective different values cost-effectiveness threshold. steeper gradient successive points frontier, higher ICER treatment alternatives expensive alternative considered cost-effective high value cost-effectiveness threshold assumed. Points lying cost-effectiveness frontier represent treatment alternatives considered cost-effective value cost-effectiveness threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"","code":"# S3 method for bcea ceef.plot( he, comparators = NULL, pos = c(1, 1), start.from.origins = TRUE, threshold = NULL, flip = FALSE, dominance = TRUE, relative = FALSE, print.summary = TRUE, graph = c(\"base\", \"ggplot2\"), print.plot = TRUE, ... ) ceef.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparators Vector specifying comparators included frontier analysis. must length > 1. Default NULL includes available comparators. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. start..origins Logical. frontier start origins axes? argument reset FALSE average effectiveness /costs least one comparator negative. threshold Specifies efficiency defined based willingness--pay threshold value. set NULL (default), conditions included slope increase. positive value passed argument, efficient intervention also requires ICER comparison versus last efficient strategy greater specified threshold value. negative value ignored warning. flip Logical. axes plane inverted? dominance Logical. dominance regions included plot? relative Logical. plot display absolute measures (default FALSE) differential outcomes versus reference comparator? print.summary Logical. efficiency frontier summary printed along graph? See Details additional information. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". print.plot Logical. efficiency frontier plotted? ... graph_type=\"ggplot2\" named theme object supplied, added ggplot object. Ignored graph_type=\"base\". Setting optional argument include.ICER TRUE print ICERs summary tables, produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"ceplane ggplot object containing plot. Returned graph_type=\"ggplot2\". function produces plot cost-effectiveness efficiency frontier. dots show simulated values intervention-specific distributions effectiveness costs. circles indicate average bivariate distribution, numbers referring included intervention. numbers inside circles black intervention included frontier grey otherwise. option dominance set TRUE, dominance regions plotted, indicating areas dominance. Interventions areas dominance region frontier situation extended dominance.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"Back compatibility BCEA previous versions: bcea objects include generating e c matrices BCEA versions <2.1-0. function compatible objects created previous versions. matrices can appended bcea objects obtained using previous versions, making sure class object remains unaltered. argument print.summary allows printing brief summary efficiency frontier, default TRUE. Two tables plotted, one interventions included frontier one dominated interventions. average costs clinical benefits included intervention. frontier table includes slope increase frontier non-frontier table displays dominance type dominated intervention. Please note slopes defined increment costs unit increment benefits even flip = TRUE consistency ICER definition. angle increase radians depends definition axes, .e. value given flip argument. argument relative set TRUE, graph display absolute measures costs benefits. Instead axes represent differential costs benefits compared reference intervention (indexed ref bcea() function).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"Baio G (2013). Bayesian Methods Health Economics. CRC. IQWIG (2009). “General Methods Assessment Relation Benefits Cost, Version 1.0.” Institute Quality Efficiency Health Care (IQWIG).","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"Andrea Berardi, Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot — ceef.plot.bcea","text":"","code":"## create the bcea object m for the smoking cessation example data(Smoking) m <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats) ## produce plot ceef.plot(m, graph = \"base\") #> #> Cost-effectiveness efficiency frontier summary #> #> Interventions on the efficiency frontier: #> Effectiveness Costs Increase slope Increase angle #> Self-help 0.28824 45.733 158.66 1.5645 #> Group counselling 0.72252 143.301 224.67 1.5663 #> #> Interventions not on the efficiency frontier: #> Effectiveness Costs Dominance type #> No treatment 0.00000 0.000 Extended dominance #> Individual counselling 0.48486 94.919 Extended dominance # \\donttest{ ## tweak the options ## flip axis ceef.plot(m, flip = TRUE, dominance = FALSE, start.from.origins = FALSE, print.summary = FALSE, graph = \"base\") ## or use ggplot2 instead if(require(ggplot2)){ ceef.plot(m, dominance = TRUE, start.from.origins = FALSE, pos = TRUE, print.summary = FALSE, graph = \"ggplot2\") } #> Loading required package: ggplot2 # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary table for CEEF — ceef.summary","title":"Summary table for CEEF — ceef.summary","text":"Summary table CEEF","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary table for CEEF — ceef.summary","text":"","code":"ceef.summary(he, frontier_data, frontier_params, include.ICER = FALSE, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary table for CEEF — ceef.summary","text":"bcea object containing results Bayesian modelling economic evaluation. frontier_data Frontier data frontier_params Frontier parameters include.ICER include ICER? default: FALSE ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary table for CEEF — ceef.summary","text":"Summary printed console","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Efficiency Frontier Plot By Graph Device — ceef_plot_graph","title":"Cost-effectiveness Efficiency Frontier Plot By Graph Device — ceef_plot_graph","text":"Choice base R, ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Efficiency Frontier Plot By Graph Device — ceef_plot_graph","text":"","code":"ceef_plot_ggplot(he, frontier_data, frontier_params, ...) ceef_plot_base(he, frontier_data, frontier_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Efficiency Frontier Plot By Graph Device — ceef_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. frontier_data Frontier data frontier_params Frontier parameters ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"Produces scatter plot cost-effectiveness plane, together sustainability area, function selected willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"","code":"# S3 method for bcea ceplane.plot( he, comparison = NULL, wtp = 25000, pos = c(0, 1), graph = c(\"base\", \"ggplot2\", \"plotly\"), ... ) ceplane.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison = c(1,3) comparison = 2). wtp value willingness pay parameter. used graph = \"base\" multiple comparisons. pos Parameter set position legend; single comparison plot, ICER legend position. Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. Default value c(1,1), topright corner inside plot area. graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". ... graph = \"ggplot2\" named theme object supplied, passed ggplot2 object. usual ggplot2 syntax used. Additional graphical arguments: label.pos = FALSE: place willingness pay label different position bottom graph - base ggplot2 (label plotly). line = list(color): colour specifying colour willingness--pay line. point = list(color): vector colours specifying colour(s) associated cloud points. length 1 equal number comparisons. point = list(size): vector colours specifying size(s) points. length 1 equal number comparisons. point = list(shape): vector shapes specifying type(s) points. length 1 equal number comparisons. icer = list(color): vector colours specifying colour(s) ICER points. length 1 equal number comparisons. icer = list(size): vector colours specifying size(s) ICER points. length 1 equal number comparisons. area_include: logical, include exclude cost-effectiveness acceptability area (default TRUE). area = list(color): colour specifying colour cost-effectiveness acceptability area. currency: Currency prefix cost differential values - ggplot2 . icer_annot: Annotate ICER point text label - ggplot2 .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"graph = \"ggplot2\" ggplot object, graph = \"plotly\" plotly object containing requested plot. Nothing returned graph = \"base\", default. Grey dots show simulated values joint distribution effectiveness cost differentials. larger red dot shows ICER grey area identifies sustainability area, .e. part plan simulated values willingness pay threshold. proportion points sustainability area effectively represents CEAC given value willingness pay. comparators 2 pairwise comparison specified, scatterplots graphed using different colours.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"plotly version, point_colors, ICER_colors area_color can also specified rgba colours using either [plotly]toRGB function rgba colour string, e.g. 'rgba(1, 1, 1, 1)'.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness Plane Plot — ceplane.plot.bcea","text":"","code":"## create the bcea object for the smoking cessation example data(Smoking) m <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats) ## produce the base plot ceplane.plot(m, wtp = 200, graph = \"base\") ## select only one comparator ceplane.plot(m, wtp = 200, graph = \"base\", comparison = 3) ## use ggplot2 if (requireNamespace(\"ggplot2\")) { ceplane.plot(m, wtp = 200, pos = \"right\", icer = list(size = 2), graph = \"ggplot2\") } ## plotly ceplane.plot(m, wtp = 200, graph = 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Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"","code":"# S3 method for bcea ceplane_plot_base(he, wtp = 25000, pos_legend, graph_params, ...) ceplane_plot_base(he, ...) # S3 method for bcea ceplane_plot_ggplot(he, wtp = 25000, pos_legend, graph_params, ...) ceplane_plot_ggplot(he, ...) # S3 method for bcea ceplane_plot_plotly(he, wtp = 25000, pos_legend, graph_params, ...) ceplane_plot_plotly(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. wtp Willingness pay threshold; default 25,000 pos_legend Legend position graph_params Graph parameters ggplot2 format ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"base R returns plot ggplot2 returns ggplot2 object plotly returns plot Viewer","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Plane Plot By Graph Device — ceplane_plot_graph","text":"","code":"# single comparator data(Vaccine, package = \"BCEA\") he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ceplane.plot(he, graph = \"base\") if (FALSE) { # need to provide all the defaults because thats what # ceplane.plot() does graph_params <- list(xlab = \"x-axis label\", ylab = \"y-axis label\", title = \"my title\", xlim = c(-0.002, 0.001), ylim = c(-13, 5), point = list(sizes = 1, colors = \"darkgrey\"), area = list(color = \"lightgrey\")) he$delta_e <- as.matrix(he$delta_e) he$delta_c <- as.matrix(he$delta_c) BCEA::ceplane_plot_base(he, graph_params = graph_params) ## single non-default comparator ## multiple comparators data(Smoking) graph_params <- list(xlab = \"x-axis label\", ylab = \"y-axis label\", title = \"my title\", xlim = c(-1, 2.5), ylim = c(-1, 160), point = list(sizes = 0.5, colors = grey.colors(3, start = 0.1, end = 0.7)), area = list(color = \"lightgrey\")) he <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats) BCEA::ceplane_plot_base(he, wtp = 200, pos_legend = FALSE, graph_params = graph_params) } data(Vaccine) he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ceplane.plot(he, graph = \"ggplot2\") ceplane.plot(he, wtp=10000, graph = \"ggplot2\", point = list(colors = \"blue\", sizes = 2), area = list(col = \"springgreen3\")) data(Smoking) he <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats) ceplane.plot(he, graph = \"ggplot2\") ceplane.plot(he, wtp = 200, pos = \"right\", ICER_size = 2, graph = \"ggplot2\") ceplane.plot(he, wtp = 200, pos = TRUE, graph = \"ggplot2\") ceplane.plot(he, graph = \"ggplot2\", wtp=200, theme = ggplot2::theme_linedraw())"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"Extends standard cost-effectiveness analysis modify utility function risk aversion decision maker explicitly accounted . Default vector risk aversion parameters: 1e-11, 2.5e-6, 5e-6","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"","code":"CEriskav(he) <- value # S3 method for bcea CEriskav(he) <- value # S3 method for default CEriskav(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value vector values risk aversion parameter. NULL, default values assigned R. first (smallest) value (r -> 0) produces standard analysis risk aversion.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"object class CEriskav containing following elements: Ur array containing simulated values ''known-distribution'' utilities interventions, values willingness pay parameter possible values r Urstar array containing simulated values maximum ''known-distribution'' expected utility values willingness pay parameter possible values r IBr array containing simulated values distribution Incremental Benefit values willingness pay possible values r eibr array containing Expected Incremental Benefit value willingness pay parameter possible values r vir array containing simulations Value Information value willingness pay parameter possible values r evir array containing Expected Value Information value willingness pay parameter possible values r R number possible values parameter risk aversion r r vector containing possible values parameter risk aversion r","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion — CEriskav_assign","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff,c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000 # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) ) # Define the vector of values for the risk aversion parameter, r, eg: r <- c(1e-10, 0.005, 0.020, 0.035) # Run the cost-effectiveness analysis accounting for risk aversion # \\donttest{ # uses the results of the economic evaluation # if more than 2 interventions, selects the # pairwise comparison CEriskav(m) <- r # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Plot Including a Parameter of Risk Aversion — CEriskav_plot_graph","title":"Cost-effectiveness Plot Including a Parameter of Risk Aversion — CEriskav_plot_graph","text":"Choice base R, ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Plot Including a Parameter of Risk Aversion — CEriskav_plot_graph","text":"","code":"CEriskav_plot_base(he, pos_legend) CEriskav_plot_ggplot(he, pos_legend)"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Plot Including a Parameter of Risk Aversion — CEriskav_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. pos_legend Legend position","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness summary statistics table — ce_table","title":"Cost-effectiveness summary statistics table — ce_table","text":"commonly shown journal paper.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness summary statistics table — ce_table","text":"","code":"ce_table(he, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness summary statistics table — ce_table","text":"bcea object containing results Bayesian modelling economic evaluation. wtp Willingness pay ... Additional parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness summary statistics table — ce_table","text":"","code":"data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea( e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) ) ce_table(m) #> cost eff delta.c delta.e ICER INB #> Vaccination 14.691446 -0.000805370 NA NA NA NA #> Status Quo 9.655464 -0.001055946 5.035983 0.0002505764 20097.59 1.228428"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute EVPPI — compute.evppi","title":"Compute EVPPI — compute.evppi","text":"Compute EVPPI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute EVPPI — compute.evppi","text":"","code":"# S3 method for evppi compute(he, fit.full)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute EVPPI — compute.evppi","text":"bcea object containing results Bayesian modelling economic evaluation. fit.full fit.full","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute EVPPI — compute.evppi","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","title":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","text":"Compute Cost-Effectiveness Acceptability Curve","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","text":"","code":"compute_CEAC(ib)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","text":"ib Incremental benefit","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Cost-Effectiveness Acceptability Curve — compute_CEAC","text":"Array dimensions (interv x k)","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ceaf.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Cost-Effectiveness Acceptability Frontier — compute_ceaf","title":"Compute Cost-Effectiveness Acceptability Frontier — compute_ceaf","text":"Compute Cost-Effectiveness Acceptability Frontier","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ceaf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Cost-Effectiveness Acceptability Frontier — compute_ceaf","text":"","code":"compute_ceaf(p_best_interv)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ceaf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Cost-Effectiveness Acceptability Frontier — compute_ceaf","text":"p_best_interv Probability best intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Expected Incremental Benefit — compute_EIB","title":"Compute Expected Incremental Benefit — compute_EIB","text":"summary measure useful assess potential changes decision different scenarios.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Expected Incremental Benefit — compute_EIB","text":"","code":"compute_EIB(ib)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Expected Incremental Benefit — compute_EIB","text":"ib Incremental benefit","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Expected Incremental Benefit — compute_EIB","text":"Array dimensions (interv x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Expected Incremental Benefit — compute_EIB","text":"considering pairwise comparison (e.g. simple case reference intervention \\(t = 1\\) comparator, status quo, \\(t = 0\\)), defined difference expected utilities two alternatives: $$eib := \\mbox{E}[u(e,c;1)] - \\mbox{E}[u(e,c;0)] = \\mathcal{U}^1 - \\mathcal{U}^0.$$ Analysis expected incremental benefit describes decision changes different values threshold. EIB marginalises uncertainty, incorporate describe explicitly uncertainty outcomes. overcome problem tool choice CEAC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Credible Intervals — compute_eib_cri","title":"Calculate Credible Intervals — compute_eib_cri","text":"expected incremental benefit plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Credible Intervals — compute_eib_cri","text":"","code":"compute_eib_cri(he, alpha_cri = 0.05, cri.quantile = TRUE)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Credible Intervals — compute_eib_cri","text":"bcea object containing results Bayesian modelling economic evaluation. alpha_cri Significance level, 0 - 1 cri.quantile Credible interval quantile?; logical","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Credible Intervals — compute_eib_cri","text":"cri","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Expected Value of Information — compute_EVI","title":"Compute Expected Value of Information — compute_EVI","text":"Compute Expected Value Information","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Expected Value of Information — compute_EVI","text":"","code":"compute_EVI(ol)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Expected Value of Information — compute_EVI","text":"ol Opportunity loss","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Expected Value of Information — compute_EVI","text":"EVI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute EVPPI — compute_evppi","title":"Compute EVPPI — compute_evppi","text":"Compute EVPPI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute EVPPI — compute_evppi","text":"","code":"compute_evppi(he, fit.full)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute EVPPI — compute_evppi","text":"bcea object containing results Bayesian modelling economic evaluation. fit.full fit.full","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute EVPPI — compute_evppi","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Incremental Benefit — compute_IB","title":"Compute Incremental Benefit — compute_IB","text":"Sample incremental net monetary benefit willingness--pay threshold, \\(k\\), comparator.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Incremental Benefit — compute_IB","text":"","code":"compute_IB(df_ce, k)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Incremental Benefit — compute_IB","text":"df_ce Dataframe cost effectiveness deltas k Vector willingness pay values","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Incremental Benefit — compute_IB","text":"Array dimensions (k x sim x ints)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Incremental Benefit — compute_IB","text":"Defined : $$IB = u(e,c; 1) - u(e,c; 0).$$ net benefit function used utility function, definition can re-written $$IB = k\\cdot\\Delta_e - \\Delta_c.$$","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"Defined ","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"","code":"compute_ICER(df_ce)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"df_ce Cost-effectiveness dataframe","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"ICER comparisons","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Incremental Cost-Effectiveness Ratio — compute_ICER","text":"$$ICER = \\Delta_c/\\Delta_e$$","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute k^* — compute_kstar","title":"Compute k^* — compute_kstar","text":"Find willingness--pay threshold optimal decision changes.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute k^* — compute_kstar","text":"","code":"compute_kstar(k, best, ref)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute k^* — compute_kstar","text":"k Willingness--pay grid approximation budget willing invest (vector) best Best intervention k (int) ref Reference intervention (int)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute k^* — compute_kstar","text":"integer representing intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute k^* — compute_kstar","text":"$$k^* := \\min\\{k : IB < 0 \\}$$ value break-even point corresponds ICER quantifies point decision-maker indifferent two options.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Opportunity Loss — compute_ol","title":"Compute Opportunity Loss — compute_ol","text":"difference maximum utility computed current parameter configuration (e.g. current simulation) \\(U^*\\) current utility intervention associated maximum utility overall.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Opportunity Loss — compute_ol","text":"","code":"compute_ol(Ustar, U, best)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Opportunity Loss — compute_ol","text":"Ustar Maximum utility value (sim x k) U Net monetary benefit (sim x k x interv) best Best intervention given willingness--pay (k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Opportunity Loss — compute_ol","text":"Array dimensions (sim x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Opportunity Loss — compute_ol","text":"mathematical notation, $$\\textrm{OL}(\\theta) := U^*(\\theta) - U(\\theta^\\tau)$$ \\(\\tau\\) intervention associated overall maximum utility \\(U^*(\\theta)\\) maximum utility value among comparators given simulation. opportunity loss non-negative quantity, since \\(U(\\theta^\\tau)\\leq U^*(\\theta)\\). simulations intervention cost-effective (.e. incremental benefit positive), \\(\\textrm{OL}(\\theta) = 0\\) opportunity loss, parameter configuration one obtained current simulation.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_p_best_interv.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Probability Best Intervention — compute_p_best_interv","title":"Compute Probability Best Intervention — compute_p_best_interv","text":"Compute Probability Best Intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_p_best_interv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Probability Best Intervention — compute_p_best_interv","text":"","code":"compute_p_best_interv(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_p_best_interv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Probability Best Intervention — compute_p_best_interv","text":"bcea object containing results Bayesian modelling economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute U Statistic — compute_U","title":"Compute U Statistic — compute_U","text":"Sample net (monetary) benefit willingness--pay threshold intervention.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute U Statistic — compute_U","text":"","code":"compute_U(df_ce, k)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute U Statistic — compute_U","text":"df_ce Cost-effectiveness dataframe k Willingness pay vector","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute U Statistic — compute_U","text":"Array dimensions (sim x k x ints)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ubar.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute NB for mixture of interventions — compute_Ubar","title":"Compute NB for mixture of interventions — compute_Ubar","text":"Compute NB mixture interventions","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ubar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute NB for mixture of interventions — compute_Ubar","text":"","code":"compute_Ubar(he, value)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ubar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute NB for mixture of interventions — compute_Ubar","text":"bcea object containing results Bayesian modelling economic evaluation. value Mixture weights","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Ustar Statistic — compute_Ustar","title":"Compute Ustar Statistic — compute_Ustar","text":"maximum utility value among comparators, indicating intervention produced benefits simulation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Ustar Statistic — compute_Ustar","text":"","code":"compute_Ustar(U)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Ustar Statistic — compute_Ustar","text":"U Net monetary benefit (sim x k x intervs)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Ustar Statistic — compute_Ustar","text":"Array dimensions (sim x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Value of Information — compute_vi","title":"Compute Value of Information — compute_vi","text":"difference maximum utility computed current parameter configuration \\(U^*\\) utility intervention associated maximum utility overall.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Value of Information — compute_vi","text":"","code":"compute_vi(Ustar, U)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Value of Information — compute_vi","text":"Ustar Maximum utility value (sim x k) U Net monetary benefit (sim x k x interv)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Value of Information — compute_vi","text":"Array dimensions (sim x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Value of Information — compute_vi","text":"value obtaining additional information parameter \\(\\theta\\) reduce uncertainty decisional process. defined : $$\\textrm{VI}(\\theta) := U^*(\\theta) - \\mathcal{U}^*$$ \\(U^*(\\theta)\\) maximum utility value given simulation among comparators \\(\\mathcal{U}^*(\\theta)\\) expected utility gained adoption cost-effective intervention.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/comp_names_from_.html","id":null,"dir":"Reference","previous_headings":"","what":"Comparison Names From — comp_names_from_","title":"Comparison Names From — comp_names_from_","text":"Comparison Names ","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/comp_names_from_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Comparison Names From — comp_names_from_","text":"","code":"comp_names_from_(df_ce)"},{"path":"https://n8thangreen.github.io/BCEA/reference/comp_names_from_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Comparison Names From — comp_names_from_","text":"df_ce Cost-effectiveness dataframe","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":null,"dir":"Reference","previous_headings":"","what":"Contour Plots for the Cost-Effectiveness Plane — contour.bcea","title":"Contour Plots for the Cost-Effectiveness Plane — contour.bcea","text":"Contour method objects class bcea. Produces scatterplot cost-effectiveness plane, contour-plot bivariate density differentials cost (y-axis) effectiveness (x-axis).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contour Plots for the Cost-Effectiveness Plane — contour.bcea","text":"","code":"# S3 method for bcea contour( he, pos = c(0, 1), graph = c(\"base\", \"ggplot2\"), comparison = NULL, ... ) contour(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contour Plots for the Cost-Effectiveness Plane — contour.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". plotting functions \"plotly\" implementation yet. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). ... Additional graphical arguments. usual ggplot2 syntax used regardless graph type. xlim: range plot along x-axis. NULL (default) determined range simulated values delta_e ylim: range plot along y-axis. NULL (default) determined range simulated values delta_c scale: Scales plot function observed standard deviation. levels: Numeric vector levels draw contour lines. Quantiles 0 [1] \"14 \\nLinear dependence: removing column pi.2.2.\" #> [2] \"15 \\nLinear dependence: removing column pi.2.2.\" #> [3] \"16 \\nLinear dependence: removing column pi.2.2.\" #> [4] \"17 \\nLinear dependence: removing column pi.2.2.\" #> [5] \"18 \\nLinear dependence: removing column pi.2.2.\" #> [6] \"19 \\nLinear dependence: removing column pi.2.2.\" #> [7] \"20 \\nLinear dependence: removing column pi.2.2.\" #> [8] \"21 \\nLinear dependence: removing column pi.2.2.\" #> [9] \"22 \\nLinear dependence: removing column pi.2.2.\" #> [10] \"29 \\nLinear dependence: removing column pi.2.2.\" #> [11] \"44 \\nLinear dependence: removing column pi.2.2.\" #> [12] \"45 \\nLinear dependence: removing column pi.2.2.\" #> [13] \"46 \\nLinear dependence: removing column pi.2.2.\" #> [14] \"47 \\nLinear dependence: removing column pi.2.2.\" #> [1] \"14 \\nLinear dependence: removing column pi.2.1.\" #> [2] \"15 \\nLinear dependence: removing column pi.2.1.\" #> [3] \"16 \\nLinear dependence: removing column pi.2.1.\" #> [4] \"17 \\nLinear dependence: removing column pi.2.1.\" #> [5] \"18 \\nLinear dependence: removing column pi.2.1.\" #> [6] \"19 \\nLinear dependence: removing column pi.2.1.\" #> [7] \"20 \\nLinear dependence: removing column pi.2.1.\" #> [8] \"21 \\nLinear dependence: removing column pi.2.1.\" #> [9] \"22 \\nLinear dependence: removing column pi.2.1.\" #> [10] \"29 \\nLinear dependence: removing column pi.2.1.\" #> [11] \"44 \\nLinear dependence: removing column pi.2.1.\" #> [12] \"45 \\nLinear dependence: removing column pi.2.1.\" #> [1] \"14 \\nLinear dependence: removing column pi.1.1.\" #> [2] \"15 \\nLinear dependence: removing column pi.1.1.\" #> [3] \"16 \\nLinear dependence: removing column pi.1.1.\" #> [4] \"17 \\nLinear dependence: removing column pi.1.1.\" #> [5] \"18 \\nLinear dependence: removing column pi.1.1.\" #> [6] \"19 \\nLinear dependence: removing column pi.1.1.\" #> [7] \"20 \\nLinear dependence: removing column pi.1.1.\" #> [8] \"21 \\nLinear dependence: removing column pi.1.1.\" #> [9] \"22 \\nLinear dependence: removing column pi.1.1.\" #> [10] \"29 \\nLinear dependence: removing column pi.1.1.\" #> [11] \"44 \\nLinear dependence: removing column pi.1.1.\" #> [1] \"14 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [2] \"15 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [3] \"16 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [4] \"17 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [5] \"18 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [6] \"19 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [7] \"20 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [8] \"21 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [9] \"22 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [1] \"14 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [2] \"15 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [3] \"17 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [4] \"18 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [5] \"20 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [6] \"21 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [1] \"14 \\nLinear dependence: removing column Repeat.GP.1.1.\" #> [2] \"17 \\nLinear dependence: removing column Repeat.GP.1.1.\" #> [3] \"20 \\nLinear dependence: removing column Repeat.GP.1.1.\" evppi(bcea_vacc, c(\"beta.1.\", \"beta.2.\"), inp$mat) #> $evppi #> [1] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [11] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [16] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [21] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [26] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #> [31] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.457218e-05 #> [36] 9.285676e-05 1.611413e-04 2.294259e-04 2.977105e-04 3.659951e-04 #> [41] 4.342796e-04 5.025642e-04 5.708488e-04 6.391333e-04 7.074179e-04 #> [46] 8.359579e-04 9.696656e-04 1.103373e-03 1.237081e-03 1.370789e-03 #> [51] 1.504497e-03 1.670549e-03 1.859133e-03 2.047718e-03 2.236302e-03 #> [56] 2.424887e-03 2.613472e-03 2.802056e-03 2.990641e-03 3.179226e-03 #> [61] 3.388697e-03 3.632914e-03 3.877131e-03 4.139327e-03 4.557299e-03 #> [66] 5.018772e-03 5.518913e-03 6.019054e-03 6.519195e-03 7.072839e-03 #> [71] 7.631472e-03 8.238981e-03 8.914107e-03 9.667252e-03 1.050133e-02 #> [76] 1.136910e-02 1.233047e-02 1.330277e-02 1.435958e-02 1.545712e-02 #> [81] 1.665844e-02 1.791987e-02 1.926646e-02 2.069755e-02 2.215719e-02 #> [86] 2.365858e-02 2.536231e-02 2.721777e-02 2.926248e-02 3.142167e-02 #> [91] 3.366613e-02 3.594727e-02 3.828947e-02 4.066173e-02 4.308486e-02 #> [96] 4.554495e-02 4.810142e-02 5.076759e-02 5.354944e-02 5.645627e-02 #> [101] 5.953500e-02 6.272953e-02 6.605710e-02 6.946561e-02 7.299518e-02 #> [106] 7.661519e-02 8.044882e-02 8.454452e-02 8.880028e-02 9.316356e-02 #> [111] 9.757211e-02 1.020631e-01 1.066899e-01 1.115583e-01 1.165761e-01 #> [116] 1.217236e-01 1.269848e-01 1.324089e-01 1.379159e-01 1.435288e-01 #> [121] 1.492557e-01 1.550713e-01 1.610079e-01 1.670532e-01 1.733199e-01 #> [126] 1.797311e-01 1.862892e-01 1.929569e-01 1.998673e-01 2.069650e-01 #> [131] 2.141277e-01 2.214818e-01 2.290093e-01 2.366830e-01 2.444225e-01 #> [136] 2.522186e-01 2.601369e-01 2.681379e-01 2.761958e-01 2.844056e-01 #> [141] 2.927010e-01 3.011088e-01 3.096150e-01 3.182103e-01 3.270021e-01 #> [146] 3.359932e-01 3.451267e-01 3.543843e-01 3.637561e-01 3.733013e-01 #> [151] 3.829607e-01 3.927341e-01 4.026574e-01 4.127028e-01 4.228980e-01 #> [156] 4.332371e-01 4.437379e-01 4.544137e-01 4.651813e-01 4.761334e-01 #> [161] 4.872191e-01 4.984766e-01 5.098566e-01 5.214073e-01 5.330834e-01 #> [166] 5.449266e-01 5.569304e-01 5.690002e-01 5.812412e-01 5.936338e-01 #> [171] 6.061169e-01 6.187243e-01 6.314361e-01 6.442246e-01 6.570961e-01 #> [176] 6.700288e-01 6.830269e-01 6.960982e-01 7.092648e-01 7.225512e-01 #> [181] 7.358751e-01 7.492499e-01 7.627448e-01 7.763942e-01 7.901216e-01 #> [186] 8.039977e-01 8.179865e-01 8.320791e-01 8.462536e-01 8.605366e-01 #> [191] 8.749299e-01 8.893719e-01 9.039347e-01 9.186171e-01 9.334328e-01 #> [196] 9.483216e-01 9.632936e-01 9.783535e-01 9.935304e-01 1.008831e+00 #> [201] 1.024234e+00 1.039745e+00 1.055422e+00 1.071224e+00 1.084066e+00 #> [206] 1.074809e+00 1.065660e+00 1.056641e+00 1.047729e+00 1.038873e+00 #> [211] 1.030071e+00 1.021314e+00 1.012653e+00 1.004120e+00 9.956920e-01 #> [216] 9.872935e-01 9.789248e-01 9.706356e-01 9.623851e-01 9.542037e-01 #> [221] 9.461404e-01 9.381714e-01 9.302605e-01 9.224375e-01 9.147457e-01 #> [226] 9.071125e-01 8.995306e-01 8.919891e-01 8.844713e-01 8.770460e-01 #> [231] 8.697233e-01 8.625451e-01 8.554401e-01 8.483837e-01 8.413724e-01 #> [236] 8.344054e-01 8.275071e-01 8.206365e-01 8.138200e-01 8.071060e-01 #> [241] 8.004723e-01 7.938925e-01 7.873783e-01 7.809368e-01 7.745497e-01 #> [246] 7.682354e-01 7.619523e-01 7.557112e-01 7.495586e-01 7.434515e-01 #> [251] 7.374292e-01 7.314887e-01 7.256113e-01 7.197572e-01 7.139554e-01 #> [256] 7.081910e-01 7.024605e-01 6.967964e-01 6.912702e-01 6.858125e-01 #> [261] 6.804006e-01 6.750311e-01 6.697037e-01 6.644020e-01 6.591274e-01 #> [266] 6.539432e-01 6.488189e-01 6.437494e-01 6.387192e-01 6.337315e-01 #> [271] 6.287676e-01 6.238712e-01 6.190358e-01 6.143003e-01 6.096329e-01 #> [276] 6.050127e-01 6.004741e-01 5.960047e-01 5.915586e-01 5.871403e-01 #> [281] 5.827532e-01 5.783884e-01 5.740675e-01 5.697920e-01 5.655458e-01 #> [286] 5.613352e-01 5.571441e-01 5.529855e-01 5.488534e-01 5.447722e-01 #> [291] 5.406968e-01 5.366213e-01 5.325726e-01 5.285423e-01 5.245240e-01 #> [296] 5.205150e-01 5.165845e-01 5.126795e-01 5.088076e-01 5.049948e-01 #> [301] 5.012110e-01 4.975225e-01 4.938624e-01 4.902186e-01 4.865747e-01 #> [306] 4.829573e-01 4.794062e-01 4.758743e-01 4.723592e-01 4.688739e-01 #> [311] 4.654132e-01 4.619793e-01 4.585647e-01 4.551584e-01 4.517521e-01 #> [316] 4.483486e-01 4.449656e-01 4.416163e-01 4.383141e-01 4.350518e-01 #> [321] 4.318246e-01 4.286196e-01 4.254631e-01 4.223070e-01 4.191951e-01 #> [326] 4.161302e-01 4.131096e-01 4.101110e-01 4.071347e-01 4.041827e-01 #> [331] 4.012589e-01 3.983743e-01 3.955212e-01 3.927254e-01 3.899432e-01 #> [336] 3.871855e-01 3.844689e-01 3.817796e-01 3.791394e-01 3.765335e-01 #> [341] 3.739600e-01 3.714164e-01 3.688934e-01 3.663704e-01 3.638535e-01 #> [346] 3.613474e-01 3.588413e-01 3.563576e-01 3.539089e-01 3.514843e-01 #> [351] 3.490616e-01 3.466548e-01 3.442652e-01 3.418779e-01 3.395314e-01 #> [356] 3.372204e-01 3.349244e-01 3.326430e-01 3.303822e-01 3.281223e-01 #> [361] 3.258784e-01 3.236347e-01 3.214071e-01 3.191795e-01 3.169807e-01 #> [366] 3.148009e-01 3.126353e-01 3.105210e-01 3.084279e-01 3.063603e-01 #> [371] 3.043336e-01 3.023203e-01 3.003220e-01 2.983323e-01 2.963581e-01 #> [376] 2.944046e-01 2.924985e-01 2.906183e-01 2.887512e-01 2.869099e-01 #> [381] 2.850765e-01 2.832430e-01 2.814097e-01 2.796030e-01 2.778181e-01 #> [386] 2.760596e-01 2.743042e-01 2.725626e-01 2.708210e-01 2.690877e-01 #> [391] 2.673614e-01 2.656351e-01 2.639213e-01 2.622214e-01 2.605248e-01 #> [396] 2.588283e-01 2.571434e-01 2.554615e-01 2.537810e-01 2.521252e-01 #> [401] 2.504935e-01 2.488892e-01 2.472959e-01 2.457275e-01 2.441923e-01 #> [406] 2.426613e-01 2.411526e-01 2.396684e-01 2.382291e-01 2.367951e-01 #> [411] 2.353736e-01 2.339666e-01 2.325611e-01 2.311557e-01 2.297563e-01 #> [416] 2.283653e-01 2.269863e-01 2.256259e-01 2.242938e-01 2.229744e-01 #> [421] 2.216551e-01 2.203358e-01 2.190165e-01 2.176971e-01 2.163804e-01 #> [426] 2.150968e-01 2.138344e-01 2.125824e-01 2.113339e-01 2.100859e-01 #> [431] 2.088515e-01 2.076339e-01 2.064492e-01 2.052776e-01 2.041179e-01 #> [436] 2.029666e-01 2.018338e-01 2.007256e-01 1.996347e-01 1.985587e-01 #> [441] 1.974892e-01 1.964197e-01 1.953502e-01 1.942885e-01 1.932456e-01 #> [446] 1.922157e-01 1.911862e-01 1.901613e-01 1.891454e-01 1.881421e-01 #> [451] 1.871387e-01 1.861354e-01 1.851410e-01 1.841523e-01 1.831884e-01 #> [456] 1.822613e-01 1.813469e-01 1.804353e-01 1.795236e-01 1.786235e-01 #> [461] 1.777250e-01 1.768265e-01 1.759280e-01 1.750408e-01 1.741682e-01 #> [466] 1.733006e-01 1.724417e-01 1.715828e-01 1.707240e-01 1.698655e-01 #> [471] 1.690192e-01 1.681822e-01 1.673487e-01 1.665152e-01 1.656817e-01 #> [476] 1.648482e-01 1.640322e-01 1.632500e-01 1.624679e-01 1.616857e-01 #> [481] 1.609060e-01 1.601366e-01 1.593672e-01 1.585978e-01 1.578284e-01 #> [486] 1.570590e-01 1.562896e-01 1.555302e-01 1.547736e-01 1.540170e-01 #> [491] 1.532604e-01 1.525038e-01 1.517472e-01 1.509906e-01 1.502340e-01 #> [496] 1.494882e-01 1.487427e-01 1.479973e-01 1.472519e-01 1.465064e-01 #> [501] 1.457650e-01 #> #> $index #> [1] \"beta.1.\" \"beta.2.\" #> #> $k #> [1] 0 100 200 300 400 500 600 700 800 900 1000 1100 #> [13] 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 #> [25] 2400 2500 2600 2700 2800 2900 3000 3100 3200 3300 3400 3500 #> [37] 3600 3700 3800 3900 4000 4100 4200 4300 4400 4500 4600 4700 #> [49] 4800 4900 5000 5100 5200 5300 5400 5500 5600 5700 5800 5900 #> [61] 6000 6100 6200 6300 6400 6500 6600 6700 6800 6900 7000 7100 #> [73] 7200 7300 7400 7500 7600 7700 7800 7900 8000 8100 8200 8300 #> [85] 8400 8500 8600 8700 8800 8900 9000 9100 9200 9300 9400 9500 #> [97] 9600 9700 9800 9900 10000 10100 10200 10300 10400 10500 10600 10700 #> [109] 10800 10900 11000 11100 11200 11300 11400 11500 11600 11700 11800 11900 #> [121] 12000 12100 12200 12300 12400 12500 12600 12700 12800 12900 13000 13100 #> [133] 13200 13300 13400 13500 13600 13700 13800 13900 14000 14100 14200 14300 #> [145] 14400 14500 14600 14700 14800 14900 15000 15100 15200 15300 15400 15500 #> [157] 15600 15700 15800 15900 16000 16100 16200 16300 16400 16500 16600 16700 #> [169] 16800 16900 17000 17100 17200 17300 17400 17500 17600 17700 17800 17900 #> [181] 18000 18100 18200 18300 18400 18500 18600 18700 18800 18900 19000 19100 #> [193] 19200 19300 19400 19500 19600 19700 19800 19900 20000 20100 20200 20300 #> [205] 20400 20500 20600 20700 20800 20900 21000 21100 21200 21300 21400 21500 #> [217] 21600 21700 21800 21900 22000 22100 22200 22300 22400 22500 22600 22700 #> [229] 22800 22900 23000 23100 23200 23300 23400 23500 23600 23700 23800 23900 #> [241] 24000 24100 24200 24300 24400 24500 24600 24700 24800 24900 25000 25100 #> [253] 25200 25300 25400 25500 25600 25700 25800 25900 26000 26100 26200 26300 #> [265] 26400 26500 26600 26700 26800 26900 27000 27100 27200 27300 27400 27500 #> [277] 27600 27700 27800 27900 28000 28100 28200 28300 28400 28500 28600 28700 #> [289] 28800 28900 29000 29100 29200 29300 29400 29500 29600 29700 29800 29900 #> [301] 30000 30100 30200 30300 30400 30500 30600 30700 30800 30900 31000 31100 #> [313] 31200 31300 31400 31500 31600 31700 31800 31900 32000 32100 32200 32300 #> [325] 32400 32500 32600 32700 32800 32900 33000 33100 33200 33300 33400 33500 #> [337] 33600 33700 33800 33900 34000 34100 34200 34300 34400 34500 34600 34700 #> [349] 34800 34900 35000 35100 35200 35300 35400 35500 35600 35700 35800 35900 #> [361] 36000 36100 36200 36300 36400 36500 36600 36700 36800 36900 37000 37100 #> [373] 37200 37300 37400 37500 37600 37700 37800 37900 38000 38100 38200 38300 #> [385] 38400 38500 38600 38700 38800 38900 39000 39100 39200 39300 39400 39500 #> [397] 39600 39700 39800 39900 40000 40100 40200 40300 40400 40500 40600 40700 #> [409] 40800 40900 41000 41100 41200 41300 41400 41500 41600 41700 41800 41900 #> [421] 42000 42100 42200 42300 42400 42500 42600 42700 42800 42900 43000 43100 #> [433] 43200 43300 43400 43500 43600 43700 43800 43900 44000 44100 44200 44300 #> [445] 44400 44500 44600 44700 44800 44900 45000 45100 45200 45300 45400 45500 #> [457] 45600 45700 45800 45900 46000 46100 46200 46300 46400 46500 46600 46700 #> [469] 46800 46900 47000 47100 47200 47300 47400 47500 47600 47700 47800 47900 #> [481] 48000 48100 48200 48300 48400 48500 48600 48700 48800 48900 49000 49100 #> [493] 49200 49300 49400 49500 49600 49700 49800 49900 50000 #> #> $evi #> [1] 0.03705361 0.03785587 0.03869327 0.03957261 0.04053147 0.04149032 #> [7] 0.04249547 0.04357790 0.04468983 0.04580177 0.04696679 0.04820919 #> [13] 0.04945159 0.05071720 0.05204083 0.05341006 0.05479360 0.05628177 #> [19] 0.05790349 0.05968408 0.06163395 0.06371142 0.06579055 0.06796376 #> [25] 0.07022097 0.07273816 0.07532847 0.07795127 0.08057406 0.08320268 #> [31] 0.08589638 0.08868468 0.09152601 0.09437325 0.09732932 0.10029796 #> [37] 0.10336608 0.10650386 0.10969266 0.11291187 0.11623517 0.11961608 #> [43] 0.12301796 0.12655367 0.13016897 0.13389347 0.13771150 0.14164319 #> [49] 0.14562582 0.14972633 0.15408068 0.15855398 0.16308971 0.16773032 #> [55] 0.17246620 0.17733774 0.18240585 0.18763480 0.19302758 0.19870097 #> [61] 0.20455777 0.21058793 0.21671040 0.22296970 0.22947367 0.23630862 #> [67] 0.24330447 0.25039355 0.25766800 0.26518678 0.27299674 0.28116086 #> [73] 0.28956168 0.29817600 0.30710155 0.31640030 0.32590075 0.33562984 #> [79] 0.34549466 0.35556390 0.36586862 0.37636890 0.38722042 0.39819986 #> [85] 0.40931334 0.42067277 0.43210667 0.44377365 0.45566760 0.46785113 #> [91] 0.48014364 0.49246093 0.50499804 0.51762457 0.53037623 0.54325794 #> [97] 0.55623809 0.56931969 0.58247875 0.59587792 0.60962413 0.62343508 #> [103] 0.63739430 0.65146528 0.66560388 0.67977458 0.69403487 0.70842482 #> [109] 0.72293925 0.73755255 0.75234332 0.76723566 0.78221180 0.79721725 #> [115] 0.81232276 0.82758674 0.84296480 0.85844709 0.87398274 0.88966269 #> [121] 0.90550552 0.92141819 0.93740388 0.95345109 0.96960001 0.98586393 #> [127] 1.00217636 1.01852827 1.03494208 1.05142297 1.06797820 1.08468101 #> [133] 1.10149448 1.11850610 1.13565841 1.15294406 1.17038384 1.18787278 #> [139] 1.20544370 1.22316318 1.24098583 1.25890098 1.27685790 1.29494468 #> [145] 1.31305675 1.33121282 1.34951452 1.36794968 1.38653595 1.40518347 #> [151] 1.42398997 1.44291220 1.46187667 1.48090091 1.49998386 1.51913333 #> [157] 1.53842038 1.55785893 1.57742357 1.59708499 1.61685411 1.63671449 #> [163] 1.65661087 1.67656286 1.69652816 1.71649345 1.73649008 1.75651452 #> [169] 1.77658853 1.79668980 1.81684907 1.83704353 1.85732385 1.87768519 #> [175] 1.89813142 1.91868758 1.93931951 1.95998689 1.98070096 2.00151878 #> [181] 2.02248963 2.04353027 2.06465898 2.08582184 2.10699995 2.12828009 #> [187] 2.14966652 2.17114009 2.19267665 2.21423926 2.23585252 2.25753717 #> [193] 2.27924027 2.30098203 2.32278791 2.34466165 2.36659499 2.38854408 #> [199] 2.41055686 2.43261442 2.45475044 2.47693925 2.49917875 2.52143631 #> [205] 2.54070684 2.53787999 2.53506303 2.53229036 2.52958762 2.52697130 #> [211] 2.52440752 2.52185173 2.51932424 2.51682773 2.51436495 2.51199912 #> [217] 2.50972006 2.50748163 2.50529882 2.50313412 2.50097250 2.49883204 #> [223] 2.49676430 2.49474058 2.49271685 2.49069313 2.48867961 2.48671075 #> [229] 2.48488026 2.48307698 2.48130197 2.47958992 2.47791258 2.47625725 #> [235] 2.47464086 2.47306603 2.47152238 2.46999548 2.46856835 2.46717635 #> [241] 2.46580518 2.46446047 2.46314796 2.46185416 2.46056499 2.45930632 #> [247] 2.45807492 2.45694841 2.45588064 2.45485168 2.45384951 2.45290828 #> [253] 2.45201256 2.45115357 2.45034345 2.44954816 2.44877788 2.44801321 #> [259] 2.44724855 2.44648588 2.44575366 2.44502992 2.44434475 2.44368189 #> [265] 2.44304942 2.44246395 2.44190999 2.44138249 2.44092506 2.44048420 #> [271] 2.44005376 2.43966976 2.43930692 2.43896298 2.43867630 2.43841126 #> [277] 2.43815833 2.43792271 2.43770326 2.43750768 2.43734001 2.43717648 #> [283] 2.43701295 2.43685240 2.43672731 2.43660228 2.43649001 2.43639935 #> [289] 2.43632504 2.43627307 2.43624400 2.43625683 2.43628988 2.43635282 #> [295] 2.43644101 2.43653657 2.43664089 2.43676890 2.43691328 2.43709723 #> [301] 2.43728708 2.43747693 2.43766678 2.43785663 2.43804648 2.43823633 #> [307] 2.43844428 2.43869049 2.43895177 2.43921999 2.43948822 2.43975645 #> [313] 2.44002468 2.44029291 2.44056114 2.44082937 2.44109760 2.44137953 #> [319] 2.44170391 2.44205164 2.44241982 2.44282017 2.44323328 2.44364639 #> [325] 2.44409249 2.44457061 2.44507864 2.44562174 2.44616484 2.44670794 #> [331] 2.44725127 2.44781211 2.44838171 2.44895677 2.44954877 2.45015945 #> [337] 2.45077012 2.45139695 2.45203779 2.45268564 2.45333350 2.45398136 #> [343] 2.45462922 2.45528430 2.45595980 2.45663861 2.45733166 2.45803623 #> [349] 2.45876134 2.45951256 2.46027210 2.46104954 2.46184788 2.46270278 #> [355] 2.46356630 2.46442982 2.46529334 2.46616454 2.46705682 2.46794910 #> [361] 2.46884707 2.46975739 2.47066771 2.47157803 2.47250829 2.47345963 #> [367] 2.47441097 2.47536231 2.47631365 2.47726499 2.47821633 2.47916766 #> [373] 2.48011900 2.48108604 2.48205474 2.48302345 2.48399401 2.48498194 #> [379] 2.48596986 2.48696203 2.48797773 2.48900137 2.49002501 2.49106195 #> [385] 2.49210191 2.49314802 2.49419569 2.49526209 2.49633230 2.49740252 #> [391] 2.49847482 2.49956060 2.50065377 2.50174694 2.50284011 2.50393327 #> [397] 2.50502644 2.50613166 2.50724137 2.50835109 2.50946173 2.51058409 #> [403] 2.51171324 2.51285383 2.51399442 2.51515263 2.51631514 2.51748307 #> [409] 2.51865224 2.51982141 2.52100290 2.52218565 2.52336840 2.52455115 #> [415] 2.52573390 2.52692109 2.52811219 2.52933476 2.53058895 2.53185552 #> [421] 2.53312209 2.53441819 2.53575671 2.53709574 2.53846228 2.53987875 #> [427] 2.54131648 2.54276301 2.54421702 2.54567692 2.54713737 2.54861863 #> [433] 2.55010724 2.55161064 2.55311404 2.55461744 2.55612084 2.55762424 #> [439] 2.55912764 2.56063104 2.56213444 2.56364595 2.56516720 2.56668845 #> [445] 2.56821847 2.56975283 2.57129950 2.57284964 2.57442273 2.57600088 #> [451] 2.57757903 2.57916437 2.58076660 2.58238097 2.58400753 2.58565356 #> [457] 2.58730489 2.58896026 2.59062565 2.59229225 2.59396413 2.59565871 #> [463] 2.59736472 2.59909129 2.60083599 2.60258363 2.60433128 2.60607892 #> [469] 2.60782656 2.60957421 2.61132185 2.61306949 2.61482472 2.61658627 #> [475] 2.61835210 2.62011955 2.62188699 2.62365443 2.62542187 2.62718931 #> [481] 2.62896830 2.63074890 2.63253189 2.63431941 2.63611797 2.63793732 #> [487] 2.63975777 2.64157823 2.64339903 2.64522609 2.64705546 2.64888724 #> [493] 2.65071902 2.65255080 2.65440805 2.65627824 2.65815205 2.66002586 #> [499] 2.66189967 2.66377348 2.66564729 #> #> $parameters #> [1] \"beta.1. and beta.2.\" #> #> $time #> $time$`Fitting for Effects` #> NULL #> #> $time$`Fitting for Costs` #> NULL #> #> $time$`Calculating EVPPI` #> NULL #> #> #> $method #> $method$`Methods for Effects` #> [1] \"gam\" #> #> $method$`Methods for Costs` #> [1] \"gam\" #> #> #> $fitted.costs #> ...1 #> [1,] 5.539060 0 #> [2,] 5.042096 0 #> [3,] 5.420907 0 #> [4,] 5.738414 0 #> [5,] 5.469780 0 #> [6,] 5.552250 0 #> [7,] 3.622860 0 #> [8,] 6.049415 0 #> [9,] 5.829584 0 #> [10,] 4.978065 0 #> [11,] 4.429175 0 #> [12,] 5.407992 0 #> [13,] 6.090357 0 #> [14,] 6.110306 0 #> [15,] 4.868019 0 #> [16,] 5.264807 0 #> [17,] 5.938002 0 #> [18,] 4.435298 0 #> [19,] 4.992994 0 #> [20,] 5.138636 0 #> [21,] 5.692185 0 #> [22,] 5.451911 0 #> [23,] 5.696216 0 #> [24,] 5.172949 0 #> [25,] 4.411596 0 #> [26,] 4.540321 0 #> [27,] 6.219929 0 #> [28,] 5.880996 0 #> [29,] 5.089494 0 #> [30,] 5.046960 0 #> [31,] 5.684144 0 #> [32,] 3.806804 0 #> [33,] 5.209105 0 #> [34,] 5.301564 0 #> [35,] 5.106181 0 #> [36,] 5.813065 0 #> [37,] 4.163367 0 #> [38,] 5.291885 0 #> [39,] 5.057783 0 #> [40,] 5.640054 0 #> [41,] 5.212866 0 #> [42,] 5.686964 0 #> [43,] 5.509276 0 #> [44,] 4.560746 0 #> [45,] 5.257646 0 #> [46,] 5.586814 0 #> [47,] 5.714918 0 #> [48,] 5.374325 0 #> [49,] 5.181640 0 #> [50,] 6.092522 0 #> [51,] 5.040795 0 #> [52,] 3.921548 0 #> [53,] 6.035031 0 #> [54,] 5.881642 0 #> [55,] 5.361438 0 #> [56,] 6.353782 0 #> [57,] 5.481254 0 #> [58,] 5.536473 0 #> [59,] 5.249361 0 #> [60,] 5.351821 0 #> [61,] 4.919147 0 #> [62,] 5.741087 0 #> [63,] 4.555483 0 #> [64,] 5.829663 0 #> [65,] 4.456022 0 #> [66,] 4.756325 0 #> [67,] 5.156087 0 #> [68,] 4.299859 0 #> [69,] 4.859611 0 #> [70,] 4.520524 0 #> [71,] 4.270351 0 #> [72,] 5.854351 0 #> [73,] 4.204380 0 #> [74,] 5.162071 0 #> [75,] 5.889816 0 #> [76,] 4.742549 0 #> [77,] 5.483039 0 #> [78,] 4.585330 0 #> [79,] 5.076819 0 #> [80,] 4.929809 0 #> [81,] 5.851112 0 #> [82,] 6.150576 0 #> [83,] 5.039752 0 #> [84,] 4.184469 0 #> [85,] 5.557155 0 #> [86,] 3.080087 0 #> [87,] 5.447201 0 #> [88,] 5.299291 0 #> [89,] 4.586296 0 #> [90,] 5.248812 0 #> [91,] 3.693466 0 #> [92,] 4.814863 0 #> [93,] 5.685345 0 #> [94,] 4.713743 0 #> [95,] 4.499628 0 #> [96,] 3.619984 0 #> [97,] 4.879671 0 #> [98,] 4.647593 0 #> [99,] 5.920827 0 #> [100,] 6.020268 0 #> [101,] 4.651883 0 #> [102,] 5.377532 0 #> [103,] 5.025746 0 #> [104,] 4.793158 0 #> [105,] 3.982976 0 #> [106,] 4.550494 0 #> [107,] 5.395733 0 #> [108,] 5.616294 0 #> [109,] 6.198823 0 #> [110,] 6.239986 0 #> [111,] 4.160499 0 #> [112,] 5.684216 0 #> [113,] 6.312563 0 #> [114,] 4.996849 0 #> [115,] 4.215937 0 #> [116,] 4.857364 0 #> [117,] 4.417169 0 #> [118,] 3.308411 0 #> [119,] 4.758624 0 #> [120,] 5.334703 0 #> [121,] 5.117974 0 #> [122,] 5.452513 0 #> [123,] 5.616120 0 #> [124,] 5.031824 0 #> [125,] 5.906992 0 #> [126,] 5.201315 0 #> [127,] 5.199154 0 #> [128,] 6.321913 0 #> [129,] 5.327667 0 #> [130,] 4.656508 0 #> [131,] 5.418207 0 #> [132,] 5.610628 0 #> [133,] 4.035627 0 #> [134,] 4.090451 0 #> [135,] 5.323000 0 #> [136,] 5.161108 0 #> [137,] 5.628860 0 #> [138,] 6.002209 0 #> [139,] 5.940414 0 #> [140,] 5.951488 0 #> [141,] 4.155158 0 #> [142,] 4.305728 0 #> [143,] 5.811871 0 #> [144,] 3.968335 0 #> [145,] 4.683313 0 #> [146,] 4.813944 0 #> [147,] 5.717913 0 #> [148,] 3.841804 0 #> [149,] 5.448253 0 #> [150,] 6.119106 0 #> [151,] 5.366319 0 #> [152,] 5.886763 0 #> [153,] 6.044123 0 #> [154,] 5.484369 0 #> [155,] 5.156000 0 #> [156,] 6.495019 0 #> [157,] 5.582958 0 #> [158,] 4.897245 0 #> [159,] 5.068719 0 #> [160,] 4.308136 0 #> [161,] 4.848514 0 #> [162,] 4.541059 0 #> [163,] 5.500579 0 #> [164,] 5.744604 0 #> [165,] 4.803044 0 #> [166,] 4.975764 0 #> [167,] 5.303851 0 #> [168,] 5.046976 0 #> [169,] 5.036179 0 #> [170,] 4.239431 0 #> [171,] 6.274060 0 #> [172,] 4.101257 0 #> [173,] 6.202505 0 #> [174,] 4.269867 0 #> [175,] 5.798713 0 #> [176,] 4.612728 0 #> [177,] 5.447640 0 #> [178,] 5.744765 0 #> [179,] 4.769053 0 #> [180,] 5.942330 0 #> [181,] 4.969693 0 #> [182,] 5.259084 0 #> [183,] 5.637955 0 #> [184,] 5.069238 0 #> [185,] 6.448760 0 #> [186,] 5.282090 0 #> [187,] 4.093503 0 #> [188,] 4.514099 0 #> [189,] 6.195264 0 #> [190,] 5.580690 0 #> [191,] 5.291420 0 #> [192,] 5.241205 0 #> [193,] 4.601338 0 #> [194,] 4.753936 0 #> [195,] 4.537685 0 #> [196,] 3.874407 0 #> [197,] 6.430400 0 #> [198,] 5.854666 0 #> [199,] 3.407401 0 #> [200,] 4.170614 0 #> [201,] 5.680480 0 #> [202,] 5.705507 0 #> [203,] 4.802852 0 #> [204,] 5.122291 0 #> [205,] 5.146346 0 #> [206,] 4.268045 0 #> [207,] 5.107833 0 #> [208,] 6.245946 0 #> [209,] 4.418758 0 #> [210,] 4.432187 0 #> [211,] 4.216319 0 #> [212,] 5.158100 0 #> [213,] 5.057736 0 #> [214,] 4.340614 0 #> [215,] 5.221258 0 #> [216,] 4.560710 0 #> [217,] 5.550095 0 #> [218,] 6.486105 0 #> [219,] 5.039445 0 #> [220,] 5.324935 0 #> [221,] 5.279964 0 #> [222,] 5.281816 0 #> [223,] 5.442170 0 #> [224,] 6.229507 0 #> [225,] 6.021018 0 #> [226,] 5.391458 0 #> [227,] 5.515263 0 #> [228,] 4.397692 0 #> [229,] 5.562380 0 #> [230,] 5.478475 0 #> [231,] 4.886453 0 #> [232,] 5.725884 0 #> [233,] 5.616004 0 #> [234,] 5.918790 0 #> [235,] 5.738133 0 #> [236,] 5.455824 0 #> [237,] 5.012434 0 #> [238,] 4.565855 0 #> [239,] 4.845516 0 #> [240,] 4.021740 0 #> [241,] 4.278009 0 #> [242,] 5.138652 0 #> [243,] 5.719739 0 #> [244,] 5.738399 0 #> [245,] 5.538478 0 #> [246,] 6.272382 0 #> [247,] 5.937437 0 #> [248,] 4.974556 0 #> [249,] 5.439875 0 #> [250,] 4.486892 0 #> [251,] 6.008683 0 #> [252,] 5.907629 0 #> [253,] 5.868258 0 #> [254,] 4.686085 0 #> [255,] 4.894189 0 #> [256,] 4.890957 0 #> [257,] 4.778962 0 #> [258,] 5.495533 0 #> [259,] 5.281087 0 #> [260,] 5.177108 0 #> [261,] 5.755615 0 #> [262,] 4.332822 0 #> [263,] 5.036257 0 #> [264,] 5.156907 0 #> [265,] 5.848136 0 #> [266,] 5.232010 0 #> [267,] 5.015574 0 #> [268,] 5.783073 0 #> [269,] 4.887681 0 #> [270,] 3.879547 0 #> [271,] 6.770099 0 #> [272,] 5.724358 0 #> [273,] 5.056678 0 #> [274,] 6.084100 0 #> [275,] 5.550148 0 #> [276,] 4.605858 0 #> [277,] 4.533346 0 #> [278,] 6.586399 0 #> [279,] 5.475804 0 #> [280,] 3.999786 0 #> [281,] 4.821916 0 #> [282,] 5.428940 0 #> [283,] 4.965619 0 #> [284,] 5.156365 0 #> [285,] 5.471457 0 #> [286,] 4.532253 0 #> [287,] 5.128821 0 #> [288,] 4.376665 0 #> [289,] 4.789164 0 #> [290,] 5.531183 0 #> [291,] 5.021224 0 #> [292,] 3.982473 0 #> [293,] 5.900801 0 #> [294,] 5.613028 0 #> [295,] 4.501413 0 #> [296,] 5.369793 0 #> [297,] 5.445729 0 #> [298,] 6.061728 0 #> [299,] 5.816942 0 #> [300,] 4.346642 0 #> [301,] 4.780172 0 #> [302,] 5.473171 0 #> [303,] 6.469736 0 #> [304,] 5.021547 0 #> [305,] 4.293773 0 #> [306,] 4.854917 0 #> [307,] 4.440534 0 #> [308,] 5.045389 0 #> [309,] 6.169920 0 #> [310,] 6.011702 0 #> [311,] 5.694597 0 #> [312,] 6.806970 0 #> [313,] 5.171469 0 #> [314,] 4.848553 0 #> [315,] 4.822993 0 #> [316,] 4.723618 0 #> [317,] 4.188315 0 #> [318,] 5.085701 0 #> [319,] 5.133842 0 #> [320,] 5.504449 0 #> [321,] 5.652670 0 #> [322,] 4.303276 0 #> [323,] 4.760278 0 #> [324,] 5.363784 0 #> [325,] 5.513805 0 #> [326,] 5.720236 0 #> [327,] 5.606547 0 #> [328,] 5.042369 0 #> [329,] 5.249603 0 #> [330,] 5.670955 0 #> [331,] 5.067745 0 #> [332,] 5.798864 0 #> [333,] 4.193601 0 #> [334,] 5.219055 0 #> [335,] 4.774512 0 #> [336,] 6.295426 0 #> [337,] 5.539983 0 #> [338,] 5.925282 0 #> [339,] 4.844867 0 #> [340,] 5.329984 0 #> [341,] 4.360776 0 #> [342,] 5.011469 0 #> [343,] 5.246450 0 #> [344,] 6.041730 0 #> [345,] 5.877811 0 #> [346,] 4.485290 0 #> [347,] 4.669637 0 #> [348,] 5.287495 0 #> [349,] 5.415794 0 #> [350,] 5.186906 0 #> [351,] 4.341641 0 #> [352,] 5.507438 0 #> [353,] 4.759675 0 #> [354,] 4.790569 0 #> [355,] 5.565071 0 #> [356,] 5.529241 0 #> [357,] 5.019743 0 #> [358,] 3.817816 0 #> [359,] 4.136043 0 #> [360,] 4.792871 0 #> [361,] 4.785833 0 #> [362,] 4.566678 0 #> [363,] 4.619853 0 #> [364,] 5.273044 0 #> [365,] 6.009045 0 #> [366,] 6.257479 0 #> [367,] 4.815202 0 #> [368,] 5.350234 0 #> [369,] 5.271335 0 #> [370,] 5.173084 0 #> [371,] 4.853887 0 #> [372,] 5.069487 0 #> [373,] 5.449406 0 #> [374,] 4.084644 0 #> [375,] 4.819726 0 #> [376,] 5.752519 0 #> [377,] 4.884872 0 #> [378,] 5.266579 0 #> [379,] 4.680861 0 #> [380,] 4.059640 0 #> [381,] 5.693785 0 #> [382,] 5.764346 0 #> [383,] 4.690388 0 #> [384,] 5.433501 0 #> [385,] 5.338777 0 #> [386,] 3.719235 0 #> [387,] 4.696649 0 #> [388,] 4.045716 0 #> [389,] 5.553352 0 #> [390,] 4.880399 0 #> [391,] 6.413306 0 #> [392,] 5.858073 0 #> [393,] 5.817244 0 #> [394,] 4.459265 0 #> [395,] 5.412586 0 #> [396,] 4.828992 0 #> [397,] 5.420323 0 #> [398,] 5.412676 0 #> [399,] 5.166367 0 #> [400,] 6.068486 0 #> [401,] 4.498203 0 #> [402,] 4.954914 0 #> [403,] 4.772686 0 #> [404,] 6.227671 0 #> [405,] 4.059127 0 #> [406,] 5.607136 0 #> [407,] 4.914351 0 #> [408,] 5.475194 0 #> [409,] 5.700530 0 #> [410,] 5.752671 0 #> [411,] 5.496542 0 #> [412,] 4.446989 0 #> [413,] 6.359405 0 #> [414,] 5.397362 0 #> [415,] 6.078466 0 #> [416,] 4.500891 0 #> [417,] 5.621780 0 #> [418,] 5.895566 0 #> [419,] 5.339590 0 #> [420,] 4.991008 0 #> [421,] 5.983944 0 #> [422,] 4.914116 0 #> [423,] 5.382246 0 #> [424,] 5.488874 0 #> [425,] 4.263182 0 #> [426,] 5.410025 0 #> [427,] 5.832342 0 #> [428,] 5.321753 0 #> [429,] 6.225024 0 #> [430,] 4.514543 0 #> [431,] 4.872656 0 #> [432,] 5.517399 0 #> [433,] 4.238021 0 #> [434,] 4.959032 0 #> [435,] 5.232736 0 #> [436,] 4.372003 0 #> [437,] 5.104239 0 #> [438,] 4.925713 0 #> [439,] 4.349524 0 #> [440,] 5.688364 0 #> [441,] 5.272597 0 #> [442,] 5.058150 0 #> [443,] 4.905186 0 #> [444,] 5.116603 0 #> [445,] 4.976429 0 #> [446,] 5.464805 0 #> [447,] 4.937833 0 #> [448,] 4.015283 0 #> [449,] 4.834030 0 #> [450,] 4.277362 0 #> [451,] 4.639724 0 #> [452,] 4.542908 0 #> [453,] 4.881269 0 #> [454,] 6.274252 0 #> [455,] 5.999787 0 #> [456,] 4.200631 0 #> [457,] 4.811412 0 #> [458,] 5.685109 0 #> [459,] 4.970131 0 #> [460,] 5.056624 0 #> [461,] 4.144599 0 #> [462,] 5.201531 0 #> [463,] 6.442667 0 #> [464,] 6.273942 0 #> [465,] 4.569216 0 #> [466,] 5.283486 0 #> [467,] 5.640554 0 #> [468,] 4.780244 0 #> [469,] 5.129521 0 #> [470,] 4.252870 0 #> [471,] 4.624672 0 #> [472,] 4.500011 0 #> [473,] 4.555120 0 #> [474,] 4.559019 0 #> [475,] 5.434200 0 #> [476,] 5.627229 0 #> [477,] 5.138669 0 #> [478,] 3.163727 0 #> [479,] 4.238129 0 #> [480,] 4.734084 0 #> [481,] 2.883787 0 #> [482,] 5.962383 0 #> [483,] 5.561811 0 #> [484,] 5.758392 0 #> [485,] 6.405401 0 #> [486,] 5.870972 0 #> [487,] 5.229085 0 #> [488,] 5.601664 0 #> [489,] 5.680402 0 #> [490,] 2.297103 0 #> [491,] 4.143709 0 #> [492,] 4.338752 0 #> [493,] 3.885268 0 #> [494,] 6.210636 0 #> [495,] 4.441624 0 #> [496,] 5.282179 0 #> [497,] 5.187344 0 #> [498,] 6.167678 0 #> [499,] 5.003714 0 #> [500,] 5.034583 0 #> [501,] 4.319677 0 #> [502,] 5.443731 0 #> [503,] 4.824521 0 #> [504,] 5.669115 0 #> [505,] 5.930372 0 #> [506,] 4.879292 0 #> [507,] 4.081044 0 #> [508,] 5.643155 0 #> [509,] 5.015277 0 #> [510,] 4.673759 0 #> [511,] 5.199560 0 #> [512,] 4.378753 0 #> [513,] 5.141009 0 #> [514,] 5.179615 0 #> [515,] 3.712490 0 #> [516,] 5.237060 0 #> [517,] 5.775917 0 #> [518,] 4.871324 0 #> [519,] 4.071617 0 #> [520,] 5.994396 0 #> [521,] 5.189757 0 #> [522,] 5.404872 0 #> [523,] 5.978365 0 #> [524,] 5.152467 0 #> [525,] 6.095778 0 #> [526,] 5.366312 0 #> [527,] 5.675808 0 #> [528,] 5.342810 0 #> [529,] 4.098486 0 #> [530,] 6.670130 0 #> [531,] 5.307000 0 #> [532,] 5.600181 0 #> [533,] 4.965255 0 #> [534,] 4.934772 0 #> [535,] 5.439599 0 #> [536,] 5.413750 0 #> [537,] 4.780892 0 #> [538,] 4.056149 0 #> [539,] 5.355559 0 #> [540,] 6.725569 0 #> [541,] 5.231151 0 #> [542,] 5.524804 0 #> [543,] 4.430890 0 #> [544,] 4.043705 0 #> [545,] 5.681395 0 #> [546,] 4.875717 0 #> [547,] 4.712016 0 #> [548,] 4.337122 0 #> [549,] 3.621597 0 #> [550,] 5.727064 0 #> [551,] 5.948722 0 #> [552,] 5.218302 0 #> [553,] 3.837108 0 #> [554,] 6.591027 0 #> [555,] 4.306567 0 #> [556,] 4.690052 0 #> [557,] 5.326873 0 #> [558,] 4.660598 0 #> [559,] 4.873312 0 #> [560,] 5.546182 0 #> [561,] 5.305335 0 #> [562,] 5.404546 0 #> [563,] 4.438375 0 #> [564,] 5.551944 0 #> [565,] 6.086414 0 #> [566,] 5.388023 0 #> [567,] 4.527011 0 #> [568,] 5.351103 0 #> [569,] 4.998643 0 #> [570,] 5.679968 0 #> [571,] 5.460582 0 #> [572,] 5.545496 0 #> [573,] 4.872348 0 #> [574,] 5.279657 0 #> [575,] 3.759895 0 #> [576,] 4.235306 0 #> [577,] 4.517857 0 #> [578,] 5.203312 0 #> [579,] 5.154415 0 #> [580,] 5.603429 0 #> [581,] 3.908157 0 #> [582,] 4.593308 0 #> [583,] 3.010580 0 #> [584,] 5.598714 0 #> [585,] 5.515450 0 #> [586,] 4.767280 0 #> [587,] 5.180259 0 #> [588,] 5.251397 0 #> [589,] 6.523599 0 #> [590,] 6.007581 0 #> [591,] 5.248788 0 #> [592,] 5.081477 0 #> [593,] 5.032266 0 #> [594,] 3.771727 0 #> [595,] 4.863446 0 #> [596,] 3.972018 0 #> [597,] 6.249646 0 #> [598,] 4.984692 0 #> [599,] 5.988938 0 #> [600,] 4.848311 0 #> [601,] 3.900215 0 #> [602,] 4.411661 0 #> [603,] 5.970272 0 #> [604,] 5.920707 0 #> [605,] 6.017723 0 #> [606,] 6.457222 0 #> [607,] 6.229802 0 #> [608,] 4.678008 0 #> [609,] 5.079349 0 #> [610,] 4.625489 0 #> [611,] 3.929851 0 #> [612,] 4.150531 0 #> [613,] 5.537036 0 #> [614,] 5.300277 0 #> [615,] 5.209738 0 #> [616,] 5.093470 0 #> [617,] 5.092428 0 #> [618,] 5.695194 0 #> [619,] 5.436593 0 #> [620,] 5.447769 0 #> [621,] 5.196907 0 #> [622,] 4.936854 0 #> [623,] 3.701986 0 #> [624,] 4.726856 0 #> [625,] 5.824369 0 #> [626,] 5.314725 0 #> [627,] 5.610858 0 #> [628,] 6.044890 0 #> [629,] 4.974855 0 #> [630,] 5.696964 0 #> [631,] 4.609722 0 #> [632,] 5.117806 0 #> [633,] 4.709817 0 #> [634,] 4.389617 0 #> [635,] 5.399749 0 #> [636,] 5.801023 0 #> [637,] 7.265691 0 #> [638,] 5.370188 0 #> [639,] 4.894873 0 #> [640,] 5.493457 0 #> [641,] 5.734250 0 #> [642,] 4.930295 0 #> [643,] 3.808431 0 #> [644,] 5.976408 0 #> [645,] 4.605815 0 #> [646,] 4.770862 0 #> [647,] 5.406588 0 #> [648,] 5.105629 0 #> [649,] 4.635775 0 #> [650,] 6.140334 0 #> [651,] 4.845058 0 #> [652,] 4.863513 0 #> [653,] 5.348916 0 #> [654,] 6.027477 0 #> [655,] 5.674001 0 #> [656,] 4.764467 0 #> [657,] 6.142376 0 #> [658,] 5.616845 0 #> [659,] 4.430326 0 #> [660,] 4.810077 0 #> [661,] 5.676379 0 #> [662,] 4.566416 0 #> [663,] 4.238390 0 #> [664,] 5.096798 0 #> [665,] 4.828042 0 #> [666,] 5.088630 0 #> [667,] 4.010339 0 #> [668,] 4.288057 0 #> [669,] 5.211723 0 #> [670,] 4.968670 0 #> [671,] 2.766377 0 #> [672,] 5.870460 0 #> [673,] 5.358856 0 #> [674,] 4.715795 0 #> [675,] 4.969381 0 #> [676,] 5.061035 0 #> [677,] 6.669621 0 #> [678,] 5.697250 0 #> [679,] 5.403520 0 #> [680,] 4.633642 0 #> [681,] 5.471435 0 #> [682,] 5.537645 0 #> [683,] 4.226920 0 #> [684,] 5.837938 0 #> [685,] 5.897236 0 #> [686,] 4.352410 0 #> [687,] 4.441955 0 #> [688,] 4.034040 0 #> [689,] 5.304813 0 #> [690,] 5.464001 0 #> [691,] 5.434683 0 #> [692,] 4.740093 0 #> [693,] 5.848070 0 #> [694,] 4.469613 0 #> [695,] 5.295348 0 #> [696,] 5.495367 0 #> [697,] 4.533725 0 #> [698,] 4.488026 0 #> [699,] 4.579108 0 #> [700,] 5.184222 0 #> [701,] 5.532139 0 #> [702,] 5.348684 0 #> [703,] 4.857925 0 #> [704,] 4.428535 0 #> [705,] 4.961068 0 #> [706,] 4.171562 0 #> [707,] 4.822307 0 #> [708,] 4.816405 0 #> [709,] 5.906019 0 #> [710,] 4.243828 0 #> [711,] 5.204671 0 #> [712,] 4.472506 0 #> [713,] 6.428801 0 #> [714,] 5.084785 0 #> [715,] 6.088572 0 #> [716,] 6.576893 0 #> [717,] 5.205091 0 #> [718,] 5.799816 0 #> [719,] 6.121609 0 #> [720,] 4.649332 0 #> [721,] 5.361074 0 #> [722,] 5.074634 0 #> [723,] 6.252053 0 #> [724,] 4.302907 0 #> [725,] 5.798890 0 #> 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[332,] 1.545657e-04 0 #> [333,] 4.808882e-04 0 #> [334,] 2.415266e-04 0 #> [335,] 3.091395e-04 0 #> [336,] 9.837649e-05 0 #> [337,] 1.975199e-04 0 #> [338,] 1.425942e-04 0 #> [339,] 2.957010e-04 0 #> [340,] 2.272218e-04 0 #> [341,] 3.809231e-04 0 #> [342,] 2.747640e-04 0 #> [343,] 2.403698e-04 0 #> [344,] 1.318787e-04 0 #> [345,] 1.312128e-04 0 #> [346,] 3.197906e-04 0 #> [347,] 3.511751e-04 0 #> [348,] 2.212144e-04 0 #> [349,] 2.117912e-04 0 #> [350,] 2.427150e-04 0 #> [351,] 4.194031e-04 0 #> [352,] 2.017630e-04 0 #> [353,] 3.337610e-04 0 #> [354,] 3.366892e-04 0 #> [355,] 1.869612e-04 0 #> [356,] 1.783136e-04 0 #> [357,] 2.761644e-04 0 #> [358,] 4.404260e-04 0 #> [359,] 3.888548e-04 0 #> [360,] 3.346423e-04 0 #> [361,] 3.095410e-04 0 #> [362,] 3.705765e-04 0 #> [363,] 2.982045e-04 0 #> [364,] 2.365162e-04 0 #> [365,] 1.468685e-04 0 #> [366,] 1.060493e-04 0 #> [367,] 3.314899e-04 0 #> [368,] 2.142466e-04 0 #> [369,] 2.360436e-04 0 #> [370,] 2.467690e-04 0 #> [371,] 3.199588e-04 0 #> [372,] 2.724874e-04 0 #> [373,] 2.098690e-04 0 #> [374,] 4.581940e-04 0 #> [375,] 3.303119e-04 0 #> [376,] 1.719382e-04 0 #> [377,] 3.018979e-04 0 #> [378,] 2.338475e-04 0 #> [379,] 3.392380e-04 0 #> [380,] 5.141703e-04 0 #> [381,] 1.597795e-04 0 #> [382,] 1.703533e-04 0 #> [383,] 3.063330e-04 0 #> [384,] 2.112797e-04 0 #> [385,] 2.104327e-04 0 #> [386,] 4.561084e-04 0 #> [387,] 3.546706e-04 0 #> [388,] 3.790253e-04 0 #> [389,] 1.851800e-04 0 #> [390,] 2.509202e-04 0 #> [391,] 9.791194e-05 0 #> [392,] 1.591114e-04 0 #> [393,] 1.587649e-04 0 #> [394,] 3.304360e-04 0 #> [395,] 2.151030e-04 0 #> [396,] 3.169665e-04 0 #> [397,] 2.097342e-04 0 #> [398,] 2.082469e-04 0 #> [399,] 2.514993e-04 0 #> [400,] 1.408431e-04 0 #> [401,] 3.708359e-04 0 #> [402,] 2.876342e-04 0 #> [403,] 2.910949e-04 0 #> [404,] 6.237237e-05 0 #> [405,] 4.700322e-04 0 #> [406,] 1.675128e-04 0 #> [407,] 2.893958e-04 0 #> [408,] 2.000427e-04 0 #> [409,] 1.777377e-04 0 #> [410,] 1.421449e-04 0 #> [411,] 1.987587e-04 0 #> [412,] 4.208507e-04 0 #> [413,] 1.095658e-04 0 #> [414,] 2.104461e-04 0 #> [415,] 1.105240e-04 0 #> [416,] 4.030025e-04 0 #> [417,] 1.739236e-04 0 #> [418,] 1.475393e-04 0 #> [419,] 2.220458e-04 0 #> [420,] 2.893314e-04 0 #> [421,] 1.133567e-04 0 #> [422,] 2.663714e-04 0 #> [423,] 2.177217e-04 0 #> [424,] 2.013209e-04 0 #> [425,] 3.802255e-04 0 #> [426,] 2.089242e-04 0 #> [427,] 1.594121e-04 0 #> [428,] 2.074647e-04 0 #> [429,] 1.056375e-04 0 #> [430,] 3.992094e-04 0 #> [431,] 2.957823e-04 0 #> [432,] 1.980855e-04 0 #> [433,] 3.552283e-04 0 #> [434,] 2.407333e-04 0 #> [435,] 2.424460e-04 0 #> [436,] 3.157091e-04 0 #> [437,] 2.653137e-04 0 #> [438,] 2.873695e-04 0 #> [439,] 3.297792e-04 0 #> [440,] 1.795462e-04 0 #> [441,] 2.284686e-04 0 #> [442,] 2.721920e-04 0 #> [443,] 2.982245e-04 0 #> [444,] 2.628720e-04 0 #> [445,] 2.660297e-04 0 #> [446,] 1.982129e-04 0 #> [447,] 2.945618e-04 0 #> [448,] 3.713204e-04 0 #> [449,] 3.222343e-04 0 #> [450,] 4.282617e-04 0 #> [451,] 2.885739e-04 0 #> [452,] 3.059782e-04 0 #> [453,] 3.139352e-04 0 #> [454,] 1.013619e-04 0 #> [455,] 1.398647e-04 0 #> [456,] 4.074999e-04 0 #> [457,] 3.180464e-04 0 #> [458,] 1.799393e-04 0 #> [459,] 2.573063e-04 0 #> [460,] 2.574127e-04 0 #> [461,] 3.837571e-04 0 #> [462,] 2.483195e-04 0 #> [463,] 8.844945e-05 0 #> [464,] 1.009246e-04 0 #> [465,] 3.486304e-04 0 #> [466,] 2.195502e-04 0 #> [467,] 1.673871e-04 0 #> [468,] 3.072224e-04 0 #> [469,] 2.509982e-04 0 #> [470,] 3.241261e-04 0 #> [471,] 3.760304e-04 0 #> [472,] 4.265878e-04 0 #> [473,] 3.503995e-04 0 #> [474,] 3.487650e-04 0 #> [475,] 2.108171e-04 0 #> [476,] 1.864104e-04 0 #> [477,] 2.565999e-04 0 #> [478,] 4.998003e-04 0 #> [479,] 3.242010e-04 0 #> [480,] 3.391160e-04 0 #> [481,] 6.542318e-04 0 #> [482,] 1.043097e-04 0 #> [483,] 1.886530e-04 0 #> [484,] 1.699839e-04 0 #> [485,] 9.744109e-05 0 #> [486,] 1.597365e-04 0 #> [487,] 2.268302e-04 0 #> [488,] 1.745874e-04 0 #> [489,] 1.721114e-04 0 #> [490,] 6.828457e-04 0 #> [491,] 4.083766e-04 0 #> [492,] 3.725653e-04 0 #> [493,] 4.448790e-04 0 #> [494,] 9.974543e-05 0 #> [495,] 3.921577e-04 0 #> [496,] 2.332768e-04 0 #> [497,] 2.503837e-04 0 #> [498,] 1.296128e-04 0 #> [499,] 2.816910e-04 0 #> [500,] 2.688046e-04 0 #> [501,] 4.084550e-04 0 #> [502,] 1.983299e-04 0 #> [503,] 3.195990e-04 0 #> [504,] 1.766680e-04 0 #> [505,] 1.488132e-04 0 #> [506,] 3.017238e-04 0 #> [507,] 4.497474e-04 0 #> [508,] 1.811854e-04 0 #> [509,] 2.822359e-04 0 #> [510,] 3.233739e-04 0 #> [511,] 2.266573e-04 0 #> [512,] 3.490566e-04 0 #> [513,] 2.538166e-04 0 #> [514,] 2.524564e-04 0 #> [515,] 4.379753e-04 0 #> [516,] 2.395782e-04 0 #> [517,] 1.451555e-04 0 #> [518,] 2.918630e-04 0 #> [519,] 4.319157e-04 0 #> [520,] 1.449371e-04 0 #> [521,] 2.408186e-04 0 #> [522,] 2.159483e-04 0 #> [523,] 1.493342e-04 0 #> [524,] 2.522421e-04 0 #> [525,] 1.280854e-04 0 #> [526,] 2.202640e-04 0 #> [527,] 1.401000e-04 0 #> [528,] 2.252545e-04 0 #> [529,] 4.192547e-04 0 #> [530,] 5.856150e-05 0 #> [531,] 2.303700e-04 0 #> [532,] 1.893912e-04 0 #> [533,] 2.950516e-04 0 #> [534,] 2.922270e-04 0 #> [535,] 2.112474e-04 0 #> [536,] 2.048496e-04 0 #> [537,] 2.996888e-04 0 #> [538,] 4.096615e-04 0 #> [539,] 2.214818e-04 0 #> [540,] 7.943973e-05 0 #> [541,] 2.351309e-04 0 #> [542,] 1.932919e-04 0 #> [543,] 4.004713e-04 0 #> [544,] 4.679694e-04 0 #> [545,] 1.740668e-04 0 #> [546,] 3.057172e-04 0 #> [547,] 2.849620e-04 0 #> [548,] 3.152946e-04 0 #> [549,] 4.264678e-04 0 #> [550,] 1.693906e-04 0 #> [551,] 1.516689e-04 0 #> [552,] 2.451270e-04 0 #> [553,] 3.815506e-04 0 #> [554,] 9.082806e-05 0 #> [555,] 3.713872e-04 0 #> [556,] 2.938459e-04 0 #> [557,] 2.234872e-04 0 #> [558,] 3.254973e-04 0 #> [559,] 2.948301e-04 0 #> [560,] 1.964489e-04 0 #> [561,] 2.279314e-04 0 #> [562,] 2.139851e-04 0 #> [563,] 4.205340e-04 0 #> [564,] 1.684487e-04 0 #> [565,] 8.652966e-05 0 #> [566,] 2.182924e-04 0 #> [567,] 3.284979e-04 0 #> [568,] 2.096685e-04 0 #> [569,] 2.649464e-04 0 #> [570,] 1.787032e-04 0 #> [571,] 2.014820e-04 0 #> [572,] 1.812511e-04 0 #> [573,] 3.124171e-04 0 #> [574,] 2.313510e-04 0 #> [575,] 4.504173e-04 0 #> [576,] 4.010612e-04 0 #> [577,] 3.200226e-04 0 #> [578,] 2.361785e-04 0 #> [579,] 2.522588e-04 0 #> [580,] 1.895863e-04 0 #> [581,] 4.105023e-04 0 #> [582,] 3.295778e-04 0 #> [583,] 4.747489e-04 0 #> [584,] 1.860833e-04 0 #> [585,] 2.008891e-04 0 #> [586,] 3.391290e-04 0 #> [587,] 2.367515e-04 0 #> [588,] 2.384270e-04 0 #> [589,] 9.573496e-05 0 #> [590,] 1.394594e-04 0 #> [591,] 2.405136e-04 0 #> [592,] 2.639716e-04 0 #> [593,] 2.545882e-04 0 #> [594,] 3.801204e-04 0 #> [595,] 3.092436e-04 0 #> [596,] 3.139222e-04 0 #> [597,] 1.216780e-04 0 #> [598,] 2.801116e-04 0 #> [599,] 1.471358e-04 0 #> [600,] 2.763946e-04 0 #> [601,] 4.226094e-04 0 #> [602,] 3.462439e-04 0 #> [603,] 1.465718e-04 0 #> [604,] 1.547441e-04 0 #> [605,] 1.419883e-04 0 #> [606,] 8.878479e-05 0 #> [607,] 1.208120e-04 0 #> [608,] 3.455454e-04 0 #> [609,] 2.711902e-04 0 #> [610,] 3.651901e-04 0 #> [611,] 5.341934e-04 0 #> [612,] 4.194523e-04 0 #> [613,] 1.921831e-04 0 #> [614,] 2.283533e-04 0 #> [615,] 2.354304e-04 0 #> [616,] 2.660503e-04 0 #> [617,] 2.473591e-04 0 #> [618,] 1.785404e-04 0 #> [619,] 2.116709e-04 0 #> [620,] 2.088175e-04 0 #> [621,] 2.492634e-04 0 #> [622,] 2.931449e-04 0 #> [623,] 4.939881e-04 0 #> [624,] 3.211250e-04 0 #> [625,] 1.343169e-04 0 #> [626,] 2.262327e-04 0 #> [627,] 1.886007e-04 0 #> [628,] 1.270536e-04 0 #> [629,] 2.835931e-04 0 #> [630,] 1.673164e-04 0 #> [631,] 3.483030e-04 0 #> [632,] 2.561686e-04 0 #> [633,] 2.678034e-04 0 #> [634,] 4.027118e-04 0 #> [635,] 2.168023e-04 0 #> [636,] 1.561287e-04 0 #> [637,] 5.022265e-05 0 #> [638,] 2.188013e-04 0 #> [639,] 2.493922e-04 0 #> [640,] 2.036902e-04 0 #> [641,] 1.625068e-04 0 #> [642,] 2.533160e-04 0 #> [643,] 4.844577e-04 0 #> [644,] 1.084860e-04 0 #> [645,] 3.485843e-04 0 #> [646,] 2.637173e-04 0 #> [647,] 2.155776e-04 0 #> [648,] 2.408999e-04 0 #> [649,] 3.644609e-04 0 #> [650,] 1.095570e-04 0 #> [651,] 3.207649e-04 0 #> [652,] 3.182078e-04 0 #> [653,] 2.235293e-04 0 #> [654,] 1.450184e-04 0 #> [655,] 1.749741e-04 0 #> [656,] 2.935187e-04 0 #> [657,] 1.103301e-04 0 #> [658,] 1.877717e-04 0 #> [659,] 3.202909e-04 0 #> [660,] 2.819825e-04 0 #> [661,] 1.684689e-04 0 #> [662,] 3.581793e-04 0 #> [663,] 4.056299e-04 0 #> [664,] 2.285957e-04 0 #> [665,] 3.057567e-04 0 #> [666,] 2.526728e-04 0 #> [667,] 3.799451e-04 0 #> [668,] 3.432114e-04 0 #> [669,] 2.057191e-04 0 #> [670,] 2.542318e-04 0 #> [671,] 5.487688e-04 0 #> [672,] 1.459682e-04 0 #> [673,] 2.192567e-04 0 #> [674,] 2.841136e-04 0 #> [675,] 2.772054e-04 0 #> [676,] 2.507460e-04 0 #> [677,] 5.551042e-05 0 #> [678,] 1.756016e-04 0 #> [679,] 2.160522e-04 0 #> [680,] 3.379077e-04 0 #> [681,] 2.059498e-04 0 #> [682,] 1.934096e-04 0 #> [683,] 3.707621e-04 0 #> [684,] 1.606186e-04 0 #> [685,] 1.541098e-04 0 #> [686,] 4.069886e-04 0 #> [687,] 3.313717e-04 0 #> [688,] 3.822233e-04 0 #> [689,] 2.313821e-04 0 #> [690,] 2.059575e-04 0 #> [691,] 1.949583e-04 0 #> [692,] 2.480859e-04 0 #> [693,] 1.515401e-04 0 #> [694,] 3.304577e-04 0 #> [695,] 2.325727e-04 0 #> [696,] 2.035595e-04 0 #> [697,] 2.950899e-04 0 #> [698,] 3.682394e-04 0 #> [699,] 3.535984e-04 0 #> [700,] 2.454970e-04 0 #> [701,] 1.963656e-04 0 #> [702,] 2.244821e-04 0 #> [703,] 2.620653e-04 0 #> [704,] 2.970199e-04 0 #> [705,] 2.597082e-04 0 #> [706,] 4.604239e-04 0 #> [707,] 3.180046e-04 0 #> [708,] 2.867155e-04 0 #> [709,] 1.561886e-04 0 #> [710,] 3.509606e-04 0 #> [711,] 2.110855e-04 0 #> [712,] 3.836408e-04 0 #> [713,] 3.849819e-05 0 #> [714,] 2.698589e-04 0 #> [715,] 1.317456e-04 0 #> [716,] 7.755576e-05 0 #> [717,] 2.209131e-04 0 #> [718,] 1.587916e-04 0 #> [719,] 1.288492e-04 0 #> [720,] 3.760651e-04 0 #> [721,] 2.173689e-04 0 #> [722,] 2.521303e-04 0 #> [723,] 1.045320e-04 0 #> [724,] 3.309668e-04 0 #> [725,] 1.572216e-04 0 #> [726,] 2.228909e-04 0 #> [727,] 3.337844e-04 0 #> [728,] 1.053994e-04 0 #> [729,] 1.871177e-04 0 #> [730,] 2.518188e-04 0 #> [731,] 4.384977e-04 0 #> [732,] 5.243590e-04 0 #> [733,] 2.018604e-04 0 #> [734,] 2.395444e-04 0 #> [735,] 3.241952e-04 0 #> [736,] 1.130585e-04 0 #> [737,] 2.450688e-04 0 #> [738,] 2.493143e-05 0 #> [739,] 2.882067e-04 0 #> [740,] 4.044332e-04 0 #> [741,] 1.149805e-04 0 #> [742,] 3.730648e-04 0 #> [743,] 1.283070e-04 0 #> [744,] 3.997110e-04 0 #> [745,] 1.822030e-04 0 #> [746,] 2.716948e-04 0 #> [747,] 1.137194e-04 0 #> [748,] 3.509345e-04 0 #> [749,] 2.534691e-04 0 #> [750,] 1.623516e-04 0 #> [751,] 3.234420e-04 0 #> [752,] 1.390159e-04 0 #> [753,] 3.052435e-04 0 #> [754,] 2.163745e-04 0 #> [755,] 1.824000e-04 0 #> [756,] 2.204347e-04 0 #> [757,] 2.936416e-04 0 #> [758,] 2.374341e-04 0 #> [759,] 1.472234e-04 0 #> [760,] 4.303334e-04 0 #> [761,] 3.104332e-04 0 #> [762,] 3.033111e-04 0 #> [763,] 2.722330e-04 0 #> [764,] 3.945617e-04 0 #> [765,] 2.630174e-04 0 #> [766,] 1.966927e-04 0 #> [767,] 3.844374e-04 0 #> [768,] 3.157664e-04 0 #> [769,] 4.335259e-05 0 #> [770,] 3.141318e-04 0 #> [771,] 1.654519e-04 0 #> [772,] 3.228501e-04 0 #> [773,] 3.028235e-04 0 #> [774,] 2.356557e-04 0 #> [775,] 2.774814e-04 0 #> [776,] 2.157424e-04 0 #> [777,] 1.091757e-04 0 #> [778,] 8.044973e-05 0 #> [779,] 8.345155e-05 0 #> [780,] 1.081871e-04 0 #> [781,] 2.364508e-04 0 #> [782,] 1.332829e-04 0 #> [783,] 2.488744e-04 0 #> [784,] 1.152727e-04 0 #> [785,] 2.535754e-04 0 #> [786,] 3.100772e-04 0 #> [787,] 4.785662e-04 0 #> [788,] 3.031556e-04 0 #> [789,] 2.736506e-04 0 #> [790,] 1.996877e-04 0 #> [791,] 1.275504e-04 0 #> [792,] 1.853950e-04 0 #> [793,] 1.734803e-04 0 #> [794,] 2.672030e-04 0 #> [795,] 7.667651e-05 0 #> [796,] 2.112453e-04 0 #> [797,] 3.736929e-04 0 #> [798,] 5.560811e-04 0 #> [799,] 1.790343e-04 0 #> [800,] 1.746465e-04 0 #> [801,] 2.447328e-04 0 #> [802,] 1.693475e-04 0 #> [803,] 1.674992e-04 0 #> [804,] 6.930171e-05 0 #> [805,] 8.983587e-05 0 #> [806,] 2.634435e-04 0 #> [807,] 2.582486e-04 0 #> [808,] 2.556380e-04 0 #> [809,] 1.995702e-04 0 #> [810,] 2.323220e-04 0 #> [811,] 2.857051e-04 0 #> [812,] 1.570781e-04 0 #> [813,] 2.510821e-04 0 #> [814,] 1.558716e-04 0 #> [815,] 2.963195e-04 0 #> [816,] 4.929957e-05 0 #> [817,] 2.629739e-04 0 #> [818,] 3.690959e-04 0 #> [819,] 3.209644e-04 0 #> [820,] 5.537454e-04 0 #> [821,] 1.403553e-04 0 #> [822,] 1.646922e-04 0 #> [823,] 4.237362e-04 0 #> [824,] 2.988110e-04 0 #> [825,] 3.215684e-04 0 #> [826,] 2.762804e-04 0 #> [827,] 1.348681e-04 0 #> [828,] 3.215862e-04 0 #> [829,] 1.280123e-04 0 #> [830,] 1.836707e-04 0 #> [831,] 3.014668e-04 0 #> [832,] 2.678450e-04 0 #> [833,] 3.514266e-04 0 #> [834,] 1.878796e-04 0 #> [835,] 2.105712e-04 0 #> [836,] 3.305988e-04 0 #> [837,] 6.751337e-05 0 #> [838,] 3.685690e-04 0 #> [839,] 1.003287e-04 0 #> [840,] 1.851440e-04 0 #> [841,] 1.603199e-04 0 #> [842,] 1.468121e-04 0 #> [843,] 3.060096e-04 0 #> [844,] 3.520104e-04 0 #> [845,] 5.563269e-04 0 #> [846,] 1.859809e-04 0 #> [847,] 1.993243e-04 0 #> [848,] 4.688514e-04 0 #> [849,] 2.369772e-04 0 #> [850,] 3.941052e-04 0 #> [851,] 1.504409e-04 0 #> [852,] 2.960572e-04 0 #> [853,] 2.108035e-04 0 #> [854,] 2.246083e-04 0 #> [855,] 2.852168e-04 0 #> [856,] 1.423209e-04 0 #> [857,] 3.518317e-04 0 #> [858,] 2.797742e-04 0 #> [859,] 2.222195e-04 0 #> [860,] 2.281903e-04 0 #> [861,] 1.316371e-04 0 #> [862,] 3.027514e-04 0 #> [863,] 2.942336e-04 0 #> [864,] 3.196133e-04 0 #> [865,] 2.178491e-04 0 #> [866,] 3.078019e-04 0 #> [867,] 1.659707e-04 0 #> [868,] 1.574956e-04 0 #> [869,] 2.561883e-04 0 #> [870,] 3.813491e-04 0 #> [871,] 1.841328e-04 0 #> [872,] 1.878828e-04 0 #> [873,] 2.876409e-04 0 #> [874,] 2.834307e-04 0 #> [875,] 2.445162e-04 0 #> [876,] 1.625989e-04 0 #> [877,] 3.593802e-04 0 #> [878,] 1.990678e-04 0 #> [879,] 1.946178e-04 0 #> [880,] 1.917611e-04 0 #> [881,] 1.932140e-04 0 #> [882,] 2.130751e-04 0 #> [883,] 2.530181e-04 0 #> [884,] 1.422756e-04 0 #> [885,] 2.391618e-04 0 #> [886,] 2.545553e-04 0 #> [887,] 2.307330e-04 0 #> [888,] 7.217122e-05 0 #> [889,] 4.158472e-04 0 #> [890,] 1.999728e-04 0 #> [891,] 2.276761e-04 0 #> [892,] 3.742574e-04 0 #> [893,] 2.460132e-04 0 #> [894,] 2.837380e-04 0 #> [895,] 2.418482e-04 0 #> [896,] 4.452901e-04 0 #> [897,] 2.472185e-04 0 #> [898,] 1.484630e-04 0 #> [899,] 2.565810e-04 0 #> [900,] 3.116468e-04 0 #> [901,] 2.057377e-04 0 #> [902,] 2.248702e-04 0 #> [903,] 2.476694e-04 0 #> [904,] 2.393110e-04 0 #> [905,] 1.758681e-04 0 #> [906,] 3.539754e-04 0 #> [907,] 2.837831e-04 0 #> [908,] 2.018909e-04 0 #> [909,] 2.859341e-04 0 #> [910,] 1.315055e-04 0 #> [911,] 3.526117e-04 0 #> [912,] 1.404419e-04 0 #> [913,] 1.636013e-04 0 #> [914,] 3.183048e-04 0 #> [915,] 1.109403e-04 0 #> [916,] 2.805868e-04 0 #> [917,] 4.248834e-05 0 #> [918,] 2.641218e-04 0 #> [919,] 1.591183e-04 0 #> [920,] 5.045417e-04 0 #> [921,] 1.781811e-04 0 #> [922,] 4.753054e-04 0 #> [923,] 2.873764e-04 0 #> [924,] 2.030911e-04 0 #> [925,] 1.672600e-04 0 #> [926,] 2.856257e-04 0 #> [927,] 4.057556e-04 0 #> [928,] 2.847504e-04 0 #> [929,] 1.221909e-04 0 #> [930,] 2.410461e-04 0 #> [931,] 1.670921e-04 0 #> [932,] 2.497373e-04 0 #> [933,] 9.214140e-05 0 #> [934,] 5.533825e-04 0 #> [935,] 2.583420e-04 0 #> [936,] 1.731495e-04 0 #> [937,] 1.590001e-04 0 #> [938,] 2.612118e-04 0 #> [939,] 1.852760e-04 0 #> [940,] 3.501000e-04 0 #> [941,] 2.984257e-04 0 #> [942,] 2.376755e-04 0 #> [943,] 3.153702e-04 0 #> [944,] 3.773165e-04 0 #> [945,] 3.644880e-04 0 #> [946,] 2.543904e-04 0 #> [947,] 2.732824e-04 0 #> [948,] 2.906559e-04 0 #> [949,] 1.327584e-04 0 #> [950,] 3.356992e-04 0 #> [951,] 3.028427e-04 0 #> [952,] 1.893701e-04 0 #> [953,] 1.358843e-04 0 #> [954,] 7.866640e-05 0 #> [955,] 3.341807e-04 0 #> [956,] 2.154368e-04 0 #> [957,] 3.409731e-04 0 #> [958,] 4.846245e-04 0 #> [959,] 2.034264e-04 0 #> [960,] 2.416117e-04 0 #> [961,] 3.012140e-04 0 #> [962,] 4.464723e-04 0 #> [963,] 3.008839e-04 0 #> [964,] 2.210974e-04 0 #> [965,] 2.549875e-04 0 #> [966,] 2.730135e-04 0 #> [967,] 4.464490e-04 0 #> [968,] 3.302712e-04 0 #> [969,] 2.318027e-04 0 #> [970,] 3.287092e-04 0 #> [971,] 1.561973e-04 0 #> [972,] 2.966892e-04 0 #> [973,] 2.479396e-04 0 #> [974,] 3.828799e-04 0 #> [975,] 2.450978e-04 0 #> [976,] 1.585412e-04 0 #> [977,] 3.925257e-04 0 #> [978,] 1.406725e-04 0 #> [979,] 2.428278e-04 0 #> [980,] 1.963504e-04 0 #> [981,] 2.996787e-04 0 #> [982,] 2.225087e-04 0 #> [983,] 3.079070e-04 0 #> [984,] 2.144225e-04 0 #> [985,] 3.831465e-04 0 #> [986,] 2.136507e-04 0 #> [987,] 9.502069e-05 0 #> [988,] 2.147885e-04 0 #> [989,] 2.199898e-04 0 #> [990,] 1.990254e-04 0 #> [991,] 2.568355e-04 0 #> [992,] 2.764570e-04 0 #> [993,] 1.339279e-04 0 #> [994,] 2.515579e-04 0 #> [995,] 3.253564e-04 0 #> [996,] 3.340186e-04 0 #> [997,] 3.056571e-04 0 #> [998,] 3.111801e-04 0 #> [999,] 2.623249e-04 0 #> [1000,] 2.901752e-04 0 #> #> $select #> [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 #> [15] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 #> [29] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 #> [43] 43 44 45 46 47 48 49 50 51 52 53 54 55 56 #> [57] 57 58 59 60 61 62 63 64 65 66 67 68 69 70 #> [71] 71 72 73 74 75 76 77 78 79 80 81 82 83 84 #> [85] 85 86 87 88 89 90 91 92 93 94 95 96 97 98 #> [99] 99 100 101 102 103 104 105 106 107 108 109 110 111 112 #> [113] 113 114 115 116 117 118 119 120 121 122 123 124 125 126 #> [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140 #> [141] 141 142 143 144 145 146 147 148 149 150 151 152 153 154 #> [155] 155 156 157 158 159 160 161 162 163 164 165 166 167 168 #> [169] 169 170 171 172 173 174 175 176 177 178 179 180 181 182 #> [183] 183 184 185 186 187 188 189 190 191 192 193 194 195 196 #> [197] 197 198 199 200 201 202 203 204 205 206 207 208 209 210 #> [211] 211 212 213 214 215 216 217 218 219 220 221 222 223 224 #> [225] 225 226 227 228 229 230 231 232 233 234 235 236 237 238 #> [239] 239 240 241 242 243 244 245 246 247 248 249 250 251 252 #> [253] 253 254 255 256 257 258 259 260 261 262 263 264 265 266 #> [267] 267 268 269 270 271 272 273 274 275 276 277 278 279 280 #> [281] 281 282 283 284 285 286 287 288 289 290 291 292 293 294 #> [295] 295 296 297 298 299 300 301 302 303 304 305 306 307 308 #> [309] 309 310 311 312 313 314 315 316 317 318 319 320 321 322 #> [323] 323 324 325 326 327 328 329 330 331 332 333 334 335 336 #> [337] 337 338 339 340 341 342 343 344 345 346 347 348 349 350 #> [351] 351 352 353 354 355 356 357 358 359 360 361 362 363 364 #> [365] 365 366 367 368 369 370 371 372 373 374 375 376 377 378 #> [379] 379 380 381 382 383 384 385 386 387 388 389 390 391 392 #> [393] 393 394 395 396 397 398 399 400 401 402 403 404 405 406 #> [407] 407 408 409 410 411 412 413 414 415 416 417 418 419 420 #> [421] 421 422 423 424 425 426 427 428 429 430 431 432 433 434 #> [435] 435 436 437 438 439 440 441 442 443 444 445 446 447 448 #> [449] 449 450 451 452 453 454 455 456 457 458 459 460 461 462 #> [463] 463 464 465 466 467 468 469 470 471 472 473 474 475 476 #> [477] 477 478 479 480 481 482 483 484 485 486 487 488 489 490 #> [491] 491 492 493 494 495 496 497 498 499 500 501 502 503 504 #> [505] 505 506 507 508 509 510 511 512 513 514 515 516 517 518 #> [519] 519 520 521 522 523 524 525 526 527 528 529 530 531 532 #> [533] 533 534 535 536 537 538 539 540 541 542 543 544 545 546 #> [547] 547 548 549 550 551 552 553 554 555 556 557 558 559 560 #> [561] 561 562 563 564 565 566 567 568 569 570 571 572 573 574 #> [575] 575 576 577 578 579 580 581 582 583 584 585 586 587 588 #> [589] 589 590 591 592 593 594 595 596 597 598 599 600 601 602 #> [603] 603 604 605 606 607 608 609 610 611 612 613 614 615 616 #> [617] 617 618 619 620 621 622 623 624 625 626 627 628 629 630 #> [631] 631 632 633 634 635 636 637 638 639 640 641 642 643 644 #> [645] 645 646 647 648 649 650 651 652 653 654 655 656 657 658 #> [659] 659 660 661 662 663 664 665 666 667 668 669 670 671 672 #> [673] 673 674 675 676 677 678 679 680 681 682 683 684 685 686 #> [687] 687 688 689 690 691 692 693 694 695 696 697 698 699 700 #> [701] 701 702 703 704 705 706 707 708 709 710 711 712 713 714 #> [715] 715 716 717 718 719 720 721 722 723 724 725 726 727 728 #> [729] 729 730 731 732 733 734 735 736 737 738 739 740 741 742 #> [743] 743 744 745 746 747 748 749 750 751 752 753 754 755 756 #> [757] 757 758 759 760 761 762 763 764 765 766 767 768 769 770 #> [771] 771 772 773 774 775 776 777 778 779 780 781 782 783 784 #> [785] 785 786 787 788 789 790 791 792 793 794 795 796 797 798 #> [799] 799 800 801 802 803 804 805 806 807 808 809 810 811 812 #> [813] 813 814 815 816 817 818 819 820 821 822 823 824 825 826 #> [827] 827 828 829 830 831 832 833 834 835 836 837 838 839 840 #> [841] 841 842 843 844 845 846 847 848 849 850 851 852 853 854 #> [855] 855 856 857 858 859 860 861 862 863 864 865 866 867 868 #> [869] 869 870 871 872 873 874 875 876 877 878 879 880 881 882 #> [883] 883 884 885 886 887 888 889 890 891 892 893 894 895 896 #> [897] 897 898 899 900 901 902 903 904 905 906 907 908 909 910 #> [911] 911 912 913 914 915 916 917 918 919 920 921 922 923 924 #> [925] 925 926 927 928 929 930 931 932 933 934 935 936 937 938 #> [939] 939 940 941 942 943 944 945 946 947 948 949 950 951 952 #> [953] 953 954 955 956 957 958 959 960 961 962 963 964 965 966 #> [967] 967 968 969 970 971 972 973 974 975 976 977 978 979 980 #> [981] 981 982 983 984 985 986 987 988 989 990 991 992 993 994 #> [995] 995 996 997 998 999 1000 #> #> $formula #> [1] \"te( beta.1.,beta.2., bs='cr')\" #> #> $pars #> [1] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [5] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [9] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [13] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [17] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [21] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [25] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [29] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [33] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [37] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [41] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [45] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [49] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [53] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [57] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [61] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [65] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [69] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [73] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [77] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [81] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [85] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [89] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [93] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [97] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [101] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [105] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [109] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [113] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [117] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [121] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [125] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [129] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [133] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [137] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [141] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [145] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [149] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [153] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [157] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [161] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [165] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [169] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [173] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [177] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [181] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [185] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [189] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [193] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [197] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [201] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [205] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [209] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [213] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [217] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [221] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [225] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [229] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [233] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [237] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [241] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [245] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [249] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [253] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [257] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [261] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [265] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [269] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [273] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [277] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [281] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [285] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [289] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [293] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [297] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [301] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [305] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [309] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [313] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [317] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [321] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [325] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [329] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [333] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [337] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [341] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [345] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [349] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [353] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [357] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [361] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [365] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [369] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [373] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [377] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [381] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [385] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [389] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [393] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [397] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [401] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [405] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [409] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [413] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [417] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [421] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [425] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [429] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [433] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [437] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [441] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [445] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [449] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [453] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [457] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [461] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [465] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [469] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [473] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [477] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [481] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [485] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [489] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [493] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [497] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [501] \"beta.1.,beta.2.\" #> #> $res #> pars k evppi #> 1 beta.1.,beta.2. 0 0.000000e+00 #> 2 beta.1.,beta.2. 100 0.000000e+00 #> 3 beta.1.,beta.2. 200 0.000000e+00 #> 4 beta.1.,beta.2. 300 0.000000e+00 #> 5 beta.1.,beta.2. 400 0.000000e+00 #> 6 beta.1.,beta.2. 500 0.000000e+00 #> 7 beta.1.,beta.2. 600 0.000000e+00 #> 8 beta.1.,beta.2. 700 0.000000e+00 #> 9 beta.1.,beta.2. 800 0.000000e+00 #> 10 beta.1.,beta.2. 900 0.000000e+00 #> 11 beta.1.,beta.2. 1000 0.000000e+00 #> 12 beta.1.,beta.2. 1100 0.000000e+00 #> 13 beta.1.,beta.2. 1200 0.000000e+00 #> 14 beta.1.,beta.2. 1300 0.000000e+00 #> 15 beta.1.,beta.2. 1400 0.000000e+00 #> 16 beta.1.,beta.2. 1500 0.000000e+00 #> 17 beta.1.,beta.2. 1600 0.000000e+00 #> 18 beta.1.,beta.2. 1700 0.000000e+00 #> 19 beta.1.,beta.2. 1800 0.000000e+00 #> 20 beta.1.,beta.2. 1900 0.000000e+00 #> 21 beta.1.,beta.2. 2000 0.000000e+00 #> 22 beta.1.,beta.2. 2100 0.000000e+00 #> 23 beta.1.,beta.2. 2200 0.000000e+00 #> 24 beta.1.,beta.2. 2300 0.000000e+00 #> 25 beta.1.,beta.2. 2400 0.000000e+00 #> 26 beta.1.,beta.2. 2500 0.000000e+00 #> 27 beta.1.,beta.2. 2600 0.000000e+00 #> 28 beta.1.,beta.2. 2700 0.000000e+00 #> 29 beta.1.,beta.2. 2800 0.000000e+00 #> 30 beta.1.,beta.2. 2900 0.000000e+00 #> 31 beta.1.,beta.2. 3000 0.000000e+00 #> 32 beta.1.,beta.2. 3100 0.000000e+00 #> 33 beta.1.,beta.2. 3200 0.000000e+00 #> 34 beta.1.,beta.2. 3300 0.000000e+00 #> 35 beta.1.,beta.2. 3400 2.457218e-05 #> 36 beta.1.,beta.2. 3500 9.285676e-05 #> 37 beta.1.,beta.2. 3600 1.611413e-04 #> 38 beta.1.,beta.2. 3700 2.294259e-04 #> 39 beta.1.,beta.2. 3800 2.977105e-04 #> 40 beta.1.,beta.2. 3900 3.659951e-04 #> 41 beta.1.,beta.2. 4000 4.342796e-04 #> 42 beta.1.,beta.2. 4100 5.025642e-04 #> 43 beta.1.,beta.2. 4200 5.708488e-04 #> 44 beta.1.,beta.2. 4300 6.391333e-04 #> 45 beta.1.,beta.2. 4400 7.074179e-04 #> 46 beta.1.,beta.2. 4500 8.359579e-04 #> 47 beta.1.,beta.2. 4600 9.696656e-04 #> 48 beta.1.,beta.2. 4700 1.103373e-03 #> 49 beta.1.,beta.2. 4800 1.237081e-03 #> 50 beta.1.,beta.2. 4900 1.370789e-03 #> 51 beta.1.,beta.2. 5000 1.504497e-03 #> 52 beta.1.,beta.2. 5100 1.670549e-03 #> 53 beta.1.,beta.2. 5200 1.859133e-03 #> 54 beta.1.,beta.2. 5300 2.047718e-03 #> 55 beta.1.,beta.2. 5400 2.236302e-03 #> 56 beta.1.,beta.2. 5500 2.424887e-03 #> 57 beta.1.,beta.2. 5600 2.613472e-03 #> 58 beta.1.,beta.2. 5700 2.802056e-03 #> 59 beta.1.,beta.2. 5800 2.990641e-03 #> 60 beta.1.,beta.2. 5900 3.179226e-03 #> 61 beta.1.,beta.2. 6000 3.388697e-03 #> 62 beta.1.,beta.2. 6100 3.632914e-03 #> 63 beta.1.,beta.2. 6200 3.877131e-03 #> 64 beta.1.,beta.2. 6300 4.139327e-03 #> 65 beta.1.,beta.2. 6400 4.557299e-03 #> 66 beta.1.,beta.2. 6500 5.018772e-03 #> 67 beta.1.,beta.2. 6600 5.518913e-03 #> 68 beta.1.,beta.2. 6700 6.019054e-03 #> 69 beta.1.,beta.2. 6800 6.519195e-03 #> 70 beta.1.,beta.2. 6900 7.072839e-03 #> 71 beta.1.,beta.2. 7000 7.631472e-03 #> 72 beta.1.,beta.2. 7100 8.238981e-03 #> 73 beta.1.,beta.2. 7200 8.914107e-03 #> 74 beta.1.,beta.2. 7300 9.667252e-03 #> 75 beta.1.,beta.2. 7400 1.050133e-02 #> 76 beta.1.,beta.2. 7500 1.136910e-02 #> 77 beta.1.,beta.2. 7600 1.233047e-02 #> 78 beta.1.,beta.2. 7700 1.330277e-02 #> 79 beta.1.,beta.2. 7800 1.435958e-02 #> 80 beta.1.,beta.2. 7900 1.545712e-02 #> 81 beta.1.,beta.2. 8000 1.665844e-02 #> 82 beta.1.,beta.2. 8100 1.791987e-02 #> 83 beta.1.,beta.2. 8200 1.926646e-02 #> 84 beta.1.,beta.2. 8300 2.069755e-02 #> 85 beta.1.,beta.2. 8400 2.215719e-02 #> 86 beta.1.,beta.2. 8500 2.365858e-02 #> 87 beta.1.,beta.2. 8600 2.536231e-02 #> 88 beta.1.,beta.2. 8700 2.721777e-02 #> 89 beta.1.,beta.2. 8800 2.926248e-02 #> 90 beta.1.,beta.2. 8900 3.142167e-02 #> 91 beta.1.,beta.2. 9000 3.366613e-02 #> 92 beta.1.,beta.2. 9100 3.594727e-02 #> 93 beta.1.,beta.2. 9200 3.828947e-02 #> 94 beta.1.,beta.2. 9300 4.066173e-02 #> 95 beta.1.,beta.2. 9400 4.308486e-02 #> 96 beta.1.,beta.2. 9500 4.554495e-02 #> 97 beta.1.,beta.2. 9600 4.810142e-02 #> 98 beta.1.,beta.2. 9700 5.076759e-02 #> 99 beta.1.,beta.2. 9800 5.354944e-02 #> 100 beta.1.,beta.2. 9900 5.645627e-02 #> 101 beta.1.,beta.2. 10000 5.953500e-02 #> 102 beta.1.,beta.2. 10100 6.272953e-02 #> 103 beta.1.,beta.2. 10200 6.605710e-02 #> 104 beta.1.,beta.2. 10300 6.946561e-02 #> 105 beta.1.,beta.2. 10400 7.299518e-02 #> 106 beta.1.,beta.2. 10500 7.661519e-02 #> 107 beta.1.,beta.2. 10600 8.044882e-02 #> 108 beta.1.,beta.2. 10700 8.454452e-02 #> 109 beta.1.,beta.2. 10800 8.880028e-02 #> 110 beta.1.,beta.2. 10900 9.316356e-02 #> 111 beta.1.,beta.2. 11000 9.757211e-02 #> 112 beta.1.,beta.2. 11100 1.020631e-01 #> 113 beta.1.,beta.2. 11200 1.066899e-01 #> 114 beta.1.,beta.2. 11300 1.115583e-01 #> 115 beta.1.,beta.2. 11400 1.165761e-01 #> 116 beta.1.,beta.2. 11500 1.217236e-01 #> 117 beta.1.,beta.2. 11600 1.269848e-01 #> 118 beta.1.,beta.2. 11700 1.324089e-01 #> 119 beta.1.,beta.2. 11800 1.379159e-01 #> 120 beta.1.,beta.2. 11900 1.435288e-01 #> 121 beta.1.,beta.2. 12000 1.492557e-01 #> 122 beta.1.,beta.2. 12100 1.550713e-01 #> 123 beta.1.,beta.2. 12200 1.610079e-01 #> 124 beta.1.,beta.2. 12300 1.670532e-01 #> 125 beta.1.,beta.2. 12400 1.733199e-01 #> 126 beta.1.,beta.2. 12500 1.797311e-01 #> 127 beta.1.,beta.2. 12600 1.862892e-01 #> 128 beta.1.,beta.2. 12700 1.929569e-01 #> 129 beta.1.,beta.2. 12800 1.998673e-01 #> 130 beta.1.,beta.2. 12900 2.069650e-01 #> 131 beta.1.,beta.2. 13000 2.141277e-01 #> 132 beta.1.,beta.2. 13100 2.214818e-01 #> 133 beta.1.,beta.2. 13200 2.290093e-01 #> 134 beta.1.,beta.2. 13300 2.366830e-01 #> 135 beta.1.,beta.2. 13400 2.444225e-01 #> 136 beta.1.,beta.2. 13500 2.522186e-01 #> 137 beta.1.,beta.2. 13600 2.601369e-01 #> 138 beta.1.,beta.2. 13700 2.681379e-01 #> 139 beta.1.,beta.2. 13800 2.761958e-01 #> 140 beta.1.,beta.2. 13900 2.844056e-01 #> 141 beta.1.,beta.2. 14000 2.927010e-01 #> 142 beta.1.,beta.2. 14100 3.011088e-01 #> 143 beta.1.,beta.2. 14200 3.096150e-01 #> 144 beta.1.,beta.2. 14300 3.182103e-01 #> 145 beta.1.,beta.2. 14400 3.270021e-01 #> 146 beta.1.,beta.2. 14500 3.359932e-01 #> 147 beta.1.,beta.2. 14600 3.451267e-01 #> 148 beta.1.,beta.2. 14700 3.543843e-01 #> 149 beta.1.,beta.2. 14800 3.637561e-01 #> 150 beta.1.,beta.2. 14900 3.733013e-01 #> 151 beta.1.,beta.2. 15000 3.829607e-01 #> 152 beta.1.,beta.2. 15100 3.927341e-01 #> 153 beta.1.,beta.2. 15200 4.026574e-01 #> 154 beta.1.,beta.2. 15300 4.127028e-01 #> 155 beta.1.,beta.2. 15400 4.228980e-01 #> 156 beta.1.,beta.2. 15500 4.332371e-01 #> 157 beta.1.,beta.2. 15600 4.437379e-01 #> 158 beta.1.,beta.2. 15700 4.544137e-01 #> 159 beta.1.,beta.2. 15800 4.651813e-01 #> 160 beta.1.,beta.2. 15900 4.761334e-01 #> 161 beta.1.,beta.2. 16000 4.872191e-01 #> 162 beta.1.,beta.2. 16100 4.984766e-01 #> 163 beta.1.,beta.2. 16200 5.098566e-01 #> 164 beta.1.,beta.2. 16300 5.214073e-01 #> 165 beta.1.,beta.2. 16400 5.330834e-01 #> 166 beta.1.,beta.2. 16500 5.449266e-01 #> 167 beta.1.,beta.2. 16600 5.569304e-01 #> 168 beta.1.,beta.2. 16700 5.690002e-01 #> 169 beta.1.,beta.2. 16800 5.812412e-01 #> 170 beta.1.,beta.2. 16900 5.936338e-01 #> 171 beta.1.,beta.2. 17000 6.061169e-01 #> 172 beta.1.,beta.2. 17100 6.187243e-01 #> 173 beta.1.,beta.2. 17200 6.314361e-01 #> 174 beta.1.,beta.2. 17300 6.442246e-01 #> 175 beta.1.,beta.2. 17400 6.570961e-01 #> 176 beta.1.,beta.2. 17500 6.700288e-01 #> 177 beta.1.,beta.2. 17600 6.830269e-01 #> 178 beta.1.,beta.2. 17700 6.960982e-01 #> 179 beta.1.,beta.2. 17800 7.092648e-01 #> 180 beta.1.,beta.2. 17900 7.225512e-01 #> 181 beta.1.,beta.2. 18000 7.358751e-01 #> 182 beta.1.,beta.2. 18100 7.492499e-01 #> 183 beta.1.,beta.2. 18200 7.627448e-01 #> 184 beta.1.,beta.2. 18300 7.763942e-01 #> 185 beta.1.,beta.2. 18400 7.901216e-01 #> 186 beta.1.,beta.2. 18500 8.039977e-01 #> 187 beta.1.,beta.2. 18600 8.179865e-01 #> 188 beta.1.,beta.2. 18700 8.320791e-01 #> 189 beta.1.,beta.2. 18800 8.462536e-01 #> 190 beta.1.,beta.2. 18900 8.605366e-01 #> 191 beta.1.,beta.2. 19000 8.749299e-01 #> 192 beta.1.,beta.2. 19100 8.893719e-01 #> 193 beta.1.,beta.2. 19200 9.039347e-01 #> 194 beta.1.,beta.2. 19300 9.186171e-01 #> 195 beta.1.,beta.2. 19400 9.334328e-01 #> 196 beta.1.,beta.2. 19500 9.483216e-01 #> 197 beta.1.,beta.2. 19600 9.632936e-01 #> 198 beta.1.,beta.2. 19700 9.783535e-01 #> 199 beta.1.,beta.2. 19800 9.935304e-01 #> 200 beta.1.,beta.2. 19900 1.008831e+00 #> 201 beta.1.,beta.2. 20000 1.024234e+00 #> 202 beta.1.,beta.2. 20100 1.039745e+00 #> 203 beta.1.,beta.2. 20200 1.055422e+00 #> 204 beta.1.,beta.2. 20300 1.071224e+00 #> 205 beta.1.,beta.2. 20400 1.084066e+00 #> 206 beta.1.,beta.2. 20500 1.074809e+00 #> 207 beta.1.,beta.2. 20600 1.065660e+00 #> 208 beta.1.,beta.2. 20700 1.056641e+00 #> 209 beta.1.,beta.2. 20800 1.047729e+00 #> 210 beta.1.,beta.2. 20900 1.038873e+00 #> 211 beta.1.,beta.2. 21000 1.030071e+00 #> 212 beta.1.,beta.2. 21100 1.021314e+00 #> 213 beta.1.,beta.2. 21200 1.012653e+00 #> 214 beta.1.,beta.2. 21300 1.004120e+00 #> 215 beta.1.,beta.2. 21400 9.956920e-01 #> 216 beta.1.,beta.2. 21500 9.872935e-01 #> 217 beta.1.,beta.2. 21600 9.789248e-01 #> 218 beta.1.,beta.2. 21700 9.706356e-01 #> 219 beta.1.,beta.2. 21800 9.623851e-01 #> 220 beta.1.,beta.2. 21900 9.542037e-01 #> 221 beta.1.,beta.2. 22000 9.461404e-01 #> 222 beta.1.,beta.2. 22100 9.381714e-01 #> 223 beta.1.,beta.2. 22200 9.302605e-01 #> 224 beta.1.,beta.2. 22300 9.224375e-01 #> 225 beta.1.,beta.2. 22400 9.147457e-01 #> 226 beta.1.,beta.2. 22500 9.071125e-01 #> 227 beta.1.,beta.2. 22600 8.995306e-01 #> 228 beta.1.,beta.2. 22700 8.919891e-01 #> 229 beta.1.,beta.2. 22800 8.844713e-01 #> 230 beta.1.,beta.2. 22900 8.770460e-01 #> 231 beta.1.,beta.2. 23000 8.697233e-01 #> 232 beta.1.,beta.2. 23100 8.625451e-01 #> 233 beta.1.,beta.2. 23200 8.554401e-01 #> 234 beta.1.,beta.2. 23300 8.483837e-01 #> 235 beta.1.,beta.2. 23400 8.413724e-01 #> 236 beta.1.,beta.2. 23500 8.344054e-01 #> 237 beta.1.,beta.2. 23600 8.275071e-01 #> 238 beta.1.,beta.2. 23700 8.206365e-01 #> 239 beta.1.,beta.2. 23800 8.138200e-01 #> 240 beta.1.,beta.2. 23900 8.071060e-01 #> 241 beta.1.,beta.2. 24000 8.004723e-01 #> 242 beta.1.,beta.2. 24100 7.938925e-01 #> 243 beta.1.,beta.2. 24200 7.873783e-01 #> 244 beta.1.,beta.2. 24300 7.809368e-01 #> 245 beta.1.,beta.2. 24400 7.745497e-01 #> 246 beta.1.,beta.2. 24500 7.682354e-01 #> 247 beta.1.,beta.2. 24600 7.619523e-01 #> 248 beta.1.,beta.2. 24700 7.557112e-01 #> 249 beta.1.,beta.2. 24800 7.495586e-01 #> 250 beta.1.,beta.2. 24900 7.434515e-01 #> 251 beta.1.,beta.2. 25000 7.374292e-01 #> 252 beta.1.,beta.2. 25100 7.314887e-01 #> 253 beta.1.,beta.2. 25200 7.256113e-01 #> 254 beta.1.,beta.2. 25300 7.197572e-01 #> 255 beta.1.,beta.2. 25400 7.139554e-01 #> 256 beta.1.,beta.2. 25500 7.081910e-01 #> 257 beta.1.,beta.2. 25600 7.024605e-01 #> 258 beta.1.,beta.2. 25700 6.967964e-01 #> 259 beta.1.,beta.2. 25800 6.912702e-01 #> 260 beta.1.,beta.2. 25900 6.858125e-01 #> 261 beta.1.,beta.2. 26000 6.804006e-01 #> 262 beta.1.,beta.2. 26100 6.750311e-01 #> 263 beta.1.,beta.2. 26200 6.697037e-01 #> 264 beta.1.,beta.2. 26300 6.644020e-01 #> 265 beta.1.,beta.2. 26400 6.591274e-01 #> 266 beta.1.,beta.2. 26500 6.539432e-01 #> 267 beta.1.,beta.2. 26600 6.488189e-01 #> 268 beta.1.,beta.2. 26700 6.437494e-01 #> 269 beta.1.,beta.2. 26800 6.387192e-01 #> 270 beta.1.,beta.2. 26900 6.337315e-01 #> 271 beta.1.,beta.2. 27000 6.287676e-01 #> 272 beta.1.,beta.2. 27100 6.238712e-01 #> 273 beta.1.,beta.2. 27200 6.190358e-01 #> 274 beta.1.,beta.2. 27300 6.143003e-01 #> 275 beta.1.,beta.2. 27400 6.096329e-01 #> 276 beta.1.,beta.2. 27500 6.050127e-01 #> 277 beta.1.,beta.2. 27600 6.004741e-01 #> 278 beta.1.,beta.2. 27700 5.960047e-01 #> 279 beta.1.,beta.2. 27800 5.915586e-01 #> 280 beta.1.,beta.2. 27900 5.871403e-01 #> 281 beta.1.,beta.2. 28000 5.827532e-01 #> 282 beta.1.,beta.2. 28100 5.783884e-01 #> 283 beta.1.,beta.2. 28200 5.740675e-01 #> 284 beta.1.,beta.2. 28300 5.697920e-01 #> 285 beta.1.,beta.2. 28400 5.655458e-01 #> 286 beta.1.,beta.2. 28500 5.613352e-01 #> 287 beta.1.,beta.2. 28600 5.571441e-01 #> 288 beta.1.,beta.2. 28700 5.529855e-01 #> 289 beta.1.,beta.2. 28800 5.488534e-01 #> 290 beta.1.,beta.2. 28900 5.447722e-01 #> 291 beta.1.,beta.2. 29000 5.406968e-01 #> 292 beta.1.,beta.2. 29100 5.366213e-01 #> 293 beta.1.,beta.2. 29200 5.325726e-01 #> 294 beta.1.,beta.2. 29300 5.285423e-01 #> 295 beta.1.,beta.2. 29400 5.245240e-01 #> 296 beta.1.,beta.2. 29500 5.205150e-01 #> 297 beta.1.,beta.2. 29600 5.165845e-01 #> 298 beta.1.,beta.2. 29700 5.126795e-01 #> 299 beta.1.,beta.2. 29800 5.088076e-01 #> 300 beta.1.,beta.2. 29900 5.049948e-01 #> 301 beta.1.,beta.2. 30000 5.012110e-01 #> 302 beta.1.,beta.2. 30100 4.975225e-01 #> 303 beta.1.,beta.2. 30200 4.938624e-01 #> 304 beta.1.,beta.2. 30300 4.902186e-01 #> 305 beta.1.,beta.2. 30400 4.865747e-01 #> 306 beta.1.,beta.2. 30500 4.829573e-01 #> 307 beta.1.,beta.2. 30600 4.794062e-01 #> 308 beta.1.,beta.2. 30700 4.758743e-01 #> 309 beta.1.,beta.2. 30800 4.723592e-01 #> 310 beta.1.,beta.2. 30900 4.688739e-01 #> 311 beta.1.,beta.2. 31000 4.654132e-01 #> 312 beta.1.,beta.2. 31100 4.619793e-01 #> 313 beta.1.,beta.2. 31200 4.585647e-01 #> 314 beta.1.,beta.2. 31300 4.551584e-01 #> 315 beta.1.,beta.2. 31400 4.517521e-01 #> 316 beta.1.,beta.2. 31500 4.483486e-01 #> 317 beta.1.,beta.2. 31600 4.449656e-01 #> 318 beta.1.,beta.2. 31700 4.416163e-01 #> 319 beta.1.,beta.2. 31800 4.383141e-01 #> 320 beta.1.,beta.2. 31900 4.350518e-01 #> 321 beta.1.,beta.2. 32000 4.318246e-01 #> 322 beta.1.,beta.2. 32100 4.286196e-01 #> 323 beta.1.,beta.2. 32200 4.254631e-01 #> 324 beta.1.,beta.2. 32300 4.223070e-01 #> 325 beta.1.,beta.2. 32400 4.191951e-01 #> 326 beta.1.,beta.2. 32500 4.161302e-01 #> 327 beta.1.,beta.2. 32600 4.131096e-01 #> 328 beta.1.,beta.2. 32700 4.101110e-01 #> 329 beta.1.,beta.2. 32800 4.071347e-01 #> 330 beta.1.,beta.2. 32900 4.041827e-01 #> 331 beta.1.,beta.2. 33000 4.012589e-01 #> 332 beta.1.,beta.2. 33100 3.983743e-01 #> 333 beta.1.,beta.2. 33200 3.955212e-01 #> 334 beta.1.,beta.2. 33300 3.927254e-01 #> 335 beta.1.,beta.2. 33400 3.899432e-01 #> 336 beta.1.,beta.2. 33500 3.871855e-01 #> 337 beta.1.,beta.2. 33600 3.844689e-01 #> 338 beta.1.,beta.2. 33700 3.817796e-01 #> 339 beta.1.,beta.2. 33800 3.791394e-01 #> 340 beta.1.,beta.2. 33900 3.765335e-01 #> 341 beta.1.,beta.2. 34000 3.739600e-01 #> 342 beta.1.,beta.2. 34100 3.714164e-01 #> 343 beta.1.,beta.2. 34200 3.688934e-01 #> 344 beta.1.,beta.2. 34300 3.663704e-01 #> 345 beta.1.,beta.2. 34400 3.638535e-01 #> 346 beta.1.,beta.2. 34500 3.613474e-01 #> 347 beta.1.,beta.2. 34600 3.588413e-01 #> 348 beta.1.,beta.2. 34700 3.563576e-01 #> 349 beta.1.,beta.2. 34800 3.539089e-01 #> 350 beta.1.,beta.2. 34900 3.514843e-01 #> 351 beta.1.,beta.2. 35000 3.490616e-01 #> 352 beta.1.,beta.2. 35100 3.466548e-01 #> 353 beta.1.,beta.2. 35200 3.442652e-01 #> 354 beta.1.,beta.2. 35300 3.418779e-01 #> 355 beta.1.,beta.2. 35400 3.395314e-01 #> 356 beta.1.,beta.2. 35500 3.372204e-01 #> 357 beta.1.,beta.2. 35600 3.349244e-01 #> 358 beta.1.,beta.2. 35700 3.326430e-01 #> 359 beta.1.,beta.2. 35800 3.303822e-01 #> 360 beta.1.,beta.2. 35900 3.281223e-01 #> 361 beta.1.,beta.2. 36000 3.258784e-01 #> 362 beta.1.,beta.2. 36100 3.236347e-01 #> 363 beta.1.,beta.2. 36200 3.214071e-01 #> 364 beta.1.,beta.2. 36300 3.191795e-01 #> 365 beta.1.,beta.2. 36400 3.169807e-01 #> 366 beta.1.,beta.2. 36500 3.148009e-01 #> 367 beta.1.,beta.2. 36600 3.126353e-01 #> 368 beta.1.,beta.2. 36700 3.105210e-01 #> 369 beta.1.,beta.2. 36800 3.084279e-01 #> 370 beta.1.,beta.2. 36900 3.063603e-01 #> 371 beta.1.,beta.2. 37000 3.043336e-01 #> 372 beta.1.,beta.2. 37100 3.023203e-01 #> 373 beta.1.,beta.2. 37200 3.003220e-01 #> 374 beta.1.,beta.2. 37300 2.983323e-01 #> 375 beta.1.,beta.2. 37400 2.963581e-01 #> 376 beta.1.,beta.2. 37500 2.944046e-01 #> 377 beta.1.,beta.2. 37600 2.924985e-01 #> 378 beta.1.,beta.2. 37700 2.906183e-01 #> 379 beta.1.,beta.2. 37800 2.887512e-01 #> 380 beta.1.,beta.2. 37900 2.869099e-01 #> 381 beta.1.,beta.2. 38000 2.850765e-01 #> 382 beta.1.,beta.2. 38100 2.832430e-01 #> 383 beta.1.,beta.2. 38200 2.814097e-01 #> 384 beta.1.,beta.2. 38300 2.796030e-01 #> 385 beta.1.,beta.2. 38400 2.778181e-01 #> 386 beta.1.,beta.2. 38500 2.760596e-01 #> 387 beta.1.,beta.2. 38600 2.743042e-01 #> 388 beta.1.,beta.2. 38700 2.725626e-01 #> 389 beta.1.,beta.2. 38800 2.708210e-01 #> 390 beta.1.,beta.2. 38900 2.690877e-01 #> 391 beta.1.,beta.2. 39000 2.673614e-01 #> 392 beta.1.,beta.2. 39100 2.656351e-01 #> 393 beta.1.,beta.2. 39200 2.639213e-01 #> 394 beta.1.,beta.2. 39300 2.622214e-01 #> 395 beta.1.,beta.2. 39400 2.605248e-01 #> 396 beta.1.,beta.2. 39500 2.588283e-01 #> 397 beta.1.,beta.2. 39600 2.571434e-01 #> 398 beta.1.,beta.2. 39700 2.554615e-01 #> 399 beta.1.,beta.2. 39800 2.537810e-01 #> 400 beta.1.,beta.2. 39900 2.521252e-01 #> 401 beta.1.,beta.2. 40000 2.504935e-01 #> 402 beta.1.,beta.2. 40100 2.488892e-01 #> 403 beta.1.,beta.2. 40200 2.472959e-01 #> 404 beta.1.,beta.2. 40300 2.457275e-01 #> 405 beta.1.,beta.2. 40400 2.441923e-01 #> 406 beta.1.,beta.2. 40500 2.426613e-01 #> 407 beta.1.,beta.2. 40600 2.411526e-01 #> 408 beta.1.,beta.2. 40700 2.396684e-01 #> 409 beta.1.,beta.2. 40800 2.382291e-01 #> 410 beta.1.,beta.2. 40900 2.367951e-01 #> 411 beta.1.,beta.2. 41000 2.353736e-01 #> 412 beta.1.,beta.2. 41100 2.339666e-01 #> 413 beta.1.,beta.2. 41200 2.325611e-01 #> 414 beta.1.,beta.2. 41300 2.311557e-01 #> 415 beta.1.,beta.2. 41400 2.297563e-01 #> 416 beta.1.,beta.2. 41500 2.283653e-01 #> 417 beta.1.,beta.2. 41600 2.269863e-01 #> 418 beta.1.,beta.2. 41700 2.256259e-01 #> 419 beta.1.,beta.2. 41800 2.242938e-01 #> 420 beta.1.,beta.2. 41900 2.229744e-01 #> 421 beta.1.,beta.2. 42000 2.216551e-01 #> 422 beta.1.,beta.2. 42100 2.203358e-01 #> 423 beta.1.,beta.2. 42200 2.190165e-01 #> 424 beta.1.,beta.2. 42300 2.176971e-01 #> 425 beta.1.,beta.2. 42400 2.163804e-01 #> 426 beta.1.,beta.2. 42500 2.150968e-01 #> 427 beta.1.,beta.2. 42600 2.138344e-01 #> 428 beta.1.,beta.2. 42700 2.125824e-01 #> 429 beta.1.,beta.2. 42800 2.113339e-01 #> 430 beta.1.,beta.2. 42900 2.100859e-01 #> 431 beta.1.,beta.2. 43000 2.088515e-01 #> 432 beta.1.,beta.2. 43100 2.076339e-01 #> 433 beta.1.,beta.2. 43200 2.064492e-01 #> 434 beta.1.,beta.2. 43300 2.052776e-01 #> 435 beta.1.,beta.2. 43400 2.041179e-01 #> 436 beta.1.,beta.2. 43500 2.029666e-01 #> 437 beta.1.,beta.2. 43600 2.018338e-01 #> 438 beta.1.,beta.2. 43700 2.007256e-01 #> 439 beta.1.,beta.2. 43800 1.996347e-01 #> 440 beta.1.,beta.2. 43900 1.985587e-01 #> 441 beta.1.,beta.2. 44000 1.974892e-01 #> 442 beta.1.,beta.2. 44100 1.964197e-01 #> 443 beta.1.,beta.2. 44200 1.953502e-01 #> 444 beta.1.,beta.2. 44300 1.942885e-01 #> 445 beta.1.,beta.2. 44400 1.932456e-01 #> 446 beta.1.,beta.2. 44500 1.922157e-01 #> 447 beta.1.,beta.2. 44600 1.911862e-01 #> 448 beta.1.,beta.2. 44700 1.901613e-01 #> 449 beta.1.,beta.2. 44800 1.891454e-01 #> 450 beta.1.,beta.2. 44900 1.881421e-01 #> 451 beta.1.,beta.2. 45000 1.871387e-01 #> 452 beta.1.,beta.2. 45100 1.861354e-01 #> 453 beta.1.,beta.2. 45200 1.851410e-01 #> 454 beta.1.,beta.2. 45300 1.841523e-01 #> 455 beta.1.,beta.2. 45400 1.831884e-01 #> 456 beta.1.,beta.2. 45500 1.822613e-01 #> 457 beta.1.,beta.2. 45600 1.813469e-01 #> 458 beta.1.,beta.2. 45700 1.804353e-01 #> 459 beta.1.,beta.2. 45800 1.795236e-01 #> 460 beta.1.,beta.2. 45900 1.786235e-01 #> 461 beta.1.,beta.2. 46000 1.777250e-01 #> 462 beta.1.,beta.2. 46100 1.768265e-01 #> 463 beta.1.,beta.2. 46200 1.759280e-01 #> 464 beta.1.,beta.2. 46300 1.750408e-01 #> 465 beta.1.,beta.2. 46400 1.741682e-01 #> 466 beta.1.,beta.2. 46500 1.733006e-01 #> 467 beta.1.,beta.2. 46600 1.724417e-01 #> 468 beta.1.,beta.2. 46700 1.715828e-01 #> 469 beta.1.,beta.2. 46800 1.707240e-01 #> 470 beta.1.,beta.2. 46900 1.698655e-01 #> 471 beta.1.,beta.2. 47000 1.690192e-01 #> 472 beta.1.,beta.2. 47100 1.681822e-01 #> 473 beta.1.,beta.2. 47200 1.673487e-01 #> 474 beta.1.,beta.2. 47300 1.665152e-01 #> 475 beta.1.,beta.2. 47400 1.656817e-01 #> 476 beta.1.,beta.2. 47500 1.648482e-01 #> 477 beta.1.,beta.2. 47600 1.640322e-01 #> 478 beta.1.,beta.2. 47700 1.632500e-01 #> 479 beta.1.,beta.2. 47800 1.624679e-01 #> 480 beta.1.,beta.2. 47900 1.616857e-01 #> 481 beta.1.,beta.2. 48000 1.609060e-01 #> 482 beta.1.,beta.2. 48100 1.601366e-01 #> 483 beta.1.,beta.2. 48200 1.593672e-01 #> 484 beta.1.,beta.2. 48300 1.585978e-01 #> 485 beta.1.,beta.2. 48400 1.578284e-01 #> 486 beta.1.,beta.2. 48500 1.570590e-01 #> 487 beta.1.,beta.2. 48600 1.562896e-01 #> 488 beta.1.,beta.2. 48700 1.555302e-01 #> 489 beta.1.,beta.2. 48800 1.547736e-01 #> 490 beta.1.,beta.2. 48900 1.540170e-01 #> 491 beta.1.,beta.2. 49000 1.532604e-01 #> 492 beta.1.,beta.2. 49100 1.525038e-01 #> 493 beta.1.,beta.2. 49200 1.517472e-01 #> 494 beta.1.,beta.2. 49300 1.509906e-01 #> 495 beta.1.,beta.2. 49400 1.502340e-01 #> 496 beta.1.,beta.2. 49500 1.494882e-01 #> 497 beta.1.,beta.2. 49600 1.487427e-01 #> 498 beta.1.,beta.2. 49700 1.479973e-01 #> 499 beta.1.,beta.2. 49800 1.472519e-01 #> 500 beta.1.,beta.2. 49900 1.465064e-01 #> 501 beta.1.,beta.2. 50000 1.457650e-01 #> #> attr(,\"class\") #> [1] \"evppi\" \"list\""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — evppi_plot_graph","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — evppi_plot_graph","text":"Base R ggplot2 versions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — evppi_plot_graph","text":"","code":"evppi_plot_base(evppi_obj, pos_legend, col = NULL, annot = FALSE) evppi_plot_ggplot(evppi_obj, pos_legend = c(0, 0.8), col = c(1, 1), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — evppi_plot_graph","text":"evppi_obj Object class evppi pos_legend Position legend col Colour annot Annotate EVPPI curve parameter names ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_qq_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Q-Q Plot — evppi_qq_plot","title":"Q-Q Plot — evppi_qq_plot","text":"Q-Q Plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_qq_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Q-Q Plot — evppi_qq_plot","text":"","code":"evppi_qq_plot(evppi, he, interv)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_residual_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual Plot — evppi_residual_plot","title":"Residual Plot — evppi_residual_plot","text":"Residual Plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_residual_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual Plot — evppi_residual_plot","text":"","code":"evppi_residual_plot(evppi, he, interv)"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Additive Model Fitting — fit.gam","title":"Gaussian Additive Model Fitting — fit.gam","text":"Gaussian Additive Model Fitting","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Additive Model Fitting — fit.gam","text":"","code":"fit.gam(parameter, inputs, x, form)"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Additive Model Fitting — fit.gam","text":"parameter Parameter inputs Inputs x Response variable form Formula","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian Additive Model Fitting — fit.gam","text":"List","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit Gaussian Process — fit.gp","title":"Fit Gaussian Process — fit.gp","text":"Fit Gaussian Process","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit Gaussian Process — fit.gp","text":"","code":"fit.gp(parameter, inputs, x, n.sim)"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit Gaussian Process — fit.gp","text":"parameter Parameters inputs Inputs x Response variable n.sim Number simulations","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit Gaussian Process — fit.gp","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit INLA — fit.inla","title":"Fit INLA — fit.inla","text":"Fit INLA","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit INLA — fit.inla","text":"","code":"fit.inla( parameter, inputs, x, mesh, data.scale, int.ord, convex.inner, convex.outer, cutoff, max.edge, h.value, family )"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit INLA — fit.inla","text":"parameter Parameters inputs Inputs x Response variable mesh Mesh data.scale data.scale int.ord int.ord convex.inner convex.inner convex.outer convex.outer cutoff Cut-max.edge Maximum edge h.value h.value family family","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit INLA — fit.inla","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"Credible interval ggplot geom — geom_cri","title":"Credible interval ggplot geom — geom_cri","text":"Credible interval ggplot geom","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Credible interval ggplot geom — geom_cri","text":"","code":"geom_cri(plot.cri = TRUE, params = NA)"},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_cri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Credible interval ggplot geom — geom_cri","text":"plot.cri plot CrI? Logical params Plot parameters including data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html","id":null,"dir":"Reference","previous_headings":"","what":"Geom Quadrant Text — geom_quad_txt","title":"Geom Quadrant Text — geom_quad_txt","text":"Geom Quadrant Text","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geom Quadrant Text — geom_quad_txt","text":"","code":"geom_quad_txt(he, graph_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geom Quadrant Text — geom_quad_txt","text":"bcea object containing results Bayesian modelling economic evaluation. graph_params Plot parameters; list","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/get_fitted_.html","id":null,"dir":"Reference","previous_headings":"","what":"Get fitted values from evppi object — get_fitted_","title":"Get fitted values from evppi object — get_fitted_","text":"Get fitted values evppi object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/get_fitted_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get fitted values from evppi object — get_fitted_","text":"","code":"get_fitted_(val, voi_methods, voi_models)"},{"path":"https://n8thangreen.github.io/BCEA/reference/get_fitted_.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get fitted values from evppi object — get_fitted_","text":"matrix","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":null,"dir":"Reference","previous_headings":"","what":"GrassmannOptim — GrassmannOptim","title":"GrassmannOptim — GrassmannOptim","text":"function taken GrassmannOptim package Kofi Placid Adragni Seongho Wu https://cran.r-project.org/web/packages/GrassmannOptim/index.html","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GrassmannOptim — GrassmannOptim","text":"","code":"GrassmannOptim( objfun, W, sim_anneal = FALSE, temp_init = 20, cooling_rate = 2, max_iter_sa = 100, eps_conv = 1e-05, max_iter = 100, eps_grad = 1e-05, eps_f = .Machine$double.eps, verbose = FALSE )"},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GrassmannOptim — GrassmannOptim","text":"objfun objfun W W sim_anneal sim_anneal temp_init temp_init cooling_rate cooling_rate max_iter_sa max_iter_sa eps_conv eps_conv max_iter max_iter eps_grad eps_grad eps_f eps_f verbose verbose","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GrassmannOptim — GrassmannOptim","text":"List","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"Plots distribution Incremental Benefit (IB) given value willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"","code":"# S3 method for bcea ib.plot( he, comparison = NULL, wtp = 25000, bw = \"bcv\", n = 512, xlim = NULL, graph = c(\"base\", \"ggplot2\"), ... ) ib.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison case multiple interventions, specifies one used comparison reference. Default value NULL forces R consider first non-reference intervention comparator. Controls comparator used 2 interventions present wtp value willingness pay threshold. Default value 25000. bw Identifies smoothing bandwidth used construct kernel estimation IB density. n number equally spaced points density estimated. xlim limits plot x-axis. graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"ib ggplot object containing requested plot. Returned graph=\"ggplot2\". function produces plot distribution Incremental Benefit given value willingness pay parameter. dashed area indicates positive part distribution (.e. reference cost-effective comparator).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incremental Benefit (IB) Distribution Plot — ib.plot.bcea","text":"","code":"data(\"Vaccine\") he <- BCEA::bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ib.plot(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ib_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"IB plot base R version — ib_plot_base","title":"IB plot base R version — ib_plot_base","text":"Choice base R, ggplot2","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IB plot base R version — ib_plot_base","text":"","code":"ib_plot_base(he, comparison, wtp, bw, n, xlim) ib_plot_ggplot(he, comparison, wtp, bw, n, xlim)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ib_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"IB plot base R version — ib_plot_base","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Comparison intervention wtp Willingness pay bw band width n Number xlim x-axis limits","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Information-Rank Plot for bcea Class — info.rank.bcea","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"Produces plot similar tornado plot, based analysis EVPPI. parameter value willingness--pay threshold, barchart plotted describe ratio EVPPI (specific parameter) EVPI. represents relative `importance' parameter terms expected value information.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"","code":"# S3 method for bcea info.rank( he, inp, wtp = NULL, howManyPars = NA, graph = c(\"base\", \"ggplot2\", \"plotly\"), rel = TRUE, ... ) info.rank(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. inp Named list running createInputs() containing: parameter = vector parameters individual EVPPI calculated. can given string (vector strings) names numeric vector, corresponding column numbers important parameters. mat = matrix containing simulations parameters monitored call JAGS BUGS. matrix column names matching names parameters values vector parameter match least one values. wtp value wtp analysis performed. specified break-even point current model used. howManyPars Optional maximum number parameters included bar plot. Includes parameters default. graph string used select graphical engine use plotting. (partial-)match one two options \"base\" \"plotly\". Default value \"base\" rel Logical argument specifies whether ratio EVPPI EVPI (rel = TRUE, default) absolute value EVPPI used analysis. ... Additional options. include graphical parameters user can specify: xlim = limits x-axis; ca = font size axis label (default = 0.7 full size). cn = font size parameter names vector (default = 0.7 full size) - base graphics . mai = margins graph (default = c(1.36, 1.5, 1,1)) - base graphics .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"base graphics: data.frame containing ranking parameters value selected summary, chosen wtp; plotly: plotly object, incorporating $rank element data.frame . function produces 'Info-rank' plot. extension standard 'Tornado plots' presents ranking model parameters terms impact expected value information. parameter, specific individual EVPPI computed used measure impact uncertainty parameter decision-making process, terms large expected value gaining information .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"Anna Heath, Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Information-Rank Plot for bcea Class — info.rank.bcea","text":"","code":"if (FALSE) { # Load the post-processed results of the MCMC simulation model # original JAGS output is can be downloaded from here # https://gianluca.statistica.it/book/bcea/code/vaccine.RData data(\"Vaccine\") m <- bcea(eff, cost) inp <- createInputs(vaccine_mat) info.rank(m, inp) info.rank(m, inp, graph = \"base\") info.rank(m, inp, graph = \"plotly\") info.rank(m, inp, graph = \"ggplot2\") }"},{"path":"https://n8thangreen.github.io/BCEA/reference/inforank_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Info Rank plot parameters — inforank_params","title":"Prepare Info Rank plot parameters — inforank_params","text":"Prepare Info Rank plot parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/inforank_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Info Rank plot parameters — inforank_params","text":"","code":"inforank_params(he, inp, wtp = NULL, rel, howManyPars, extra_args)"},{"path":"https://n8thangreen.github.io/BCEA/reference/inforank_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Info Rank plot parameters — inforank_params","text":"bcea object containing results Bayesian modelling economic evaluation. inp Inputs wtp Willingness pay rel Relative size howManyPars mnay parameters use? extra_args Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info_rank_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Info Rank Plot By Graph Device — info_rank_graph","title":"Info Rank Plot By Graph Device — info_rank_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info_rank_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Info Rank Plot By Graph Device — info_rank_graph","text":"","code":"info_rank_base(he, params) info_rank_ggplot(he, params) info_rank_plotly(params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/info_rank_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Info Rank Plot By Graph Device — info_rank_graph","text":"bcea object containing results Bayesian modelling economic evaluation. params Graph Parameters including data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Check bcea Class — is.bcea","title":"Check bcea Class — is.bcea","text":"Check bcea Class","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check bcea Class — is.bcea","text":"","code":"is.bcea(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check bcea Class — is.bcea","text":"bcea object containing results Bayesian modelling economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check bcea Class — is.bcea","text":".bcea returns TRUE FALSE depending whether argument bcea class object.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/kstar_vlines.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare K-star vertical lines — kstar_vlines","title":"Prepare K-star vertical lines — kstar_vlines","text":"Prepare K-star vertical lines","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/kstar_vlines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare K-star vertical lines — kstar_vlines","text":"","code":"kstar_vlines(he, plot_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/kstar_vlines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare K-star vertical lines — kstar_vlines","text":"bcea object containing results Bayesian modelling economic evaluation. plot_params Plots parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/line_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Labels for Plot — line_labels","title":"Create Labels for Plot — line_labels","text":"Create Labels Plot Swapped labels reference second","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/line_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Labels for Plot — line_labels","text":"","code":"line_labels(he, ...) # S3 method for default line_labels(he, ref_first = TRUE, ...) # S3 method for pairwise line_labels(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/line_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Labels for Plot — line_labels","text":"bcea object containing results Bayesian modelling economic evaluation. ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/loo_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Leave-one-out ranking — loo_rank","title":"Leave-one-out ranking — loo_rank","text":"Leave-one-ranking","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/loo_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leave-one-out ranking — loo_rank","text":"","code":"loo_rank(params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/loo_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leave-one-out ranking — loo_rank","text":"params Parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Mesh — make.mesh","title":"Make Mesh — make.mesh","text":"Fit using INLA methods.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Mesh — make.mesh","text":"","code":"make.mesh(data, convex.inner, convex.outer, cutoff, max.edge)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Mesh — make.mesh","text":"data Data convex.inner convex.inner convex.outer convex.outer cutoff Cut-value max.edge Maximum edge","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Mesh — make.mesh","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":null,"dir":"Reference","previous_headings":"","what":"INLA Fitting — make.proj","title":"INLA Fitting — make.proj","text":"INLA Fitting","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"INLA Fitting — make.proj","text":"","code":"make.proj(parameter, inputs, x, k, l)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"INLA Fitting — make.proj","text":"parameter Parameter inputs Inputs x Response variable k k l l","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"INLA Fitting — make.proj","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Report — make.report","title":"Make Report — make.report","text":"Constructs automated report output BCEA.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Report — make.report","text":"","code":"make.report(he, evppi = NULL, ext = \"pdf\", echo = FALSE, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Report — make.report","text":"bcea object containing results Bayesian modelling economic evaluation. evppi object obtained output call evppi (default NULL, essential producing report). ext string text indicate extension resulting output file. Possible options \"pdf\", \"docx\". requires use pandoc, knitr rmarkdown. echo string (default FALSE) instruct whether report also include BCEA commands used produce analyses. optional argument echo set TRUE (default = FALSE), commands also printed. ... Additional parameters. example, user can specify value willingness pay wtp, used resulting analyses (default break even point). Another additional parameter user can specify name file report written. can done simply passing optional argument filename=\"NAME\". user can also specify object including PSA simulations relevant model parameters. passed function (object psa_sims), make.report automatically construct \"Info-rank plot\", probabilistic form tornado plot, based Expected Value Partial Information. user can also specify optional argument show.tab (default=FALSE); set TRUE, table values Info-rank also shown.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Make Report — make.report","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Make Report — make.report","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make Report — make.report","text":"","code":"if (FALSE) { data(Vaccine, package = \"BCEA\") m <- bcea(eff, cost, ref = 2) make.report(m) }"},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":null,"dir":"Reference","previous_headings":"","what":"Legend Positioning — make_legend_plotly","title":"Legend Positioning — make_legend_plotly","text":"Legend Positioning","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legend Positioning — make_legend_plotly","text":"","code":"make_legend_plotly(pos_legend)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legend Positioning — make_legend_plotly","text":"pos_legend Position legend","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Legend Positioning — make_legend_plotly","text":"String","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots the probability that each intervention is the most cost-effective — mce.plot","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"function deprecated. Use ceac.plot() instead. Plots probability n_int interventions analysed cost-effective.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"","code":"mce.plot(mce, pos = c(1, 0.5), graph = c(\"base\", \"ggplot2\"), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"mce output call function multi.ce(). pos Parameter set position legend. Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, TRUE indicating use first standard FALSE use second one. Default value c(1,0.5), right inside plot area. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Optional arguments. example, possible specify colours used plot. done vector color=c(...). length vector colors needs number comparators included analysis, otherwise BCEA fall back default values (black, shades grey)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"mceplot ggplot object containing plot. Returned graph=\"ggplot2\".","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots the probability that each intervention is the most cost-effective — mce.plot","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem if (FALSE) { # Load the processed results of the MCMC simulation model data(Vaccine) # # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) plot=FALSE # inhibits graphical output ) # mce <- multi.ce(m) # uses the results of the economic analysis # mce.plot(mce, # plots the probability of being most cost-effective graph=\"base\") # using base graphics # if(require(ggplot2)){ mce.plot(mce, # the same plot graph=\"ggplot2\") # using ggplot2 instead } }"},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"Runs cost-effectiveness analysis, accounts fact one intervention present market.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"","code":"mixedAn(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"bcea object containing results Bayesian modelling economic evaluation. value vector market shares associated interventions. size number possible comparators. default, assumes uniform distribution intervention.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"Creates object class mixedAn, subclass bcea contains results health economic evaluation mixed analysis case: Ubar array simulations ''known-distribution'' mixed utilities, value discrete grid approximation willingness pay parameter OL.star array simulations distribution Opportunity Loss mixed strategy, value discrete grid approximation willingness pay parameter evi.star Expected Value Information mixed strategy, value discrete grid approximation willingness pay parameter mkt.shares vector market shares associated available intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"Baio G, Russo P (2009). “decision-theoretic framework application cost-effectiveness analysis regulatory processes.” Pharmacoeconomics, 27(8), 5--16. ISSN 20356137, doi:10.1007/bf03320526 . Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market — mixedAn<-","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) plot=FALSE) # inhibits graphical output mixedAn(m) <- NULL # uses the results of the mixed strategy # analysis (a \"mixedAn\" object) # the vector of market shares can be defined # externally. If NULL, then each of the T # interventions will have 1/T market share # produces the plots evi.plot(m)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"Computes plots probability n_int interventions analysed cost-effective cost-effectiveness acceptability frontier.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"","code":"# S3 method for bcea multi.ce(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"bcea object containing results Bayesian modelling economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"Original bcea object (list) class \"pairwise\" additional: p_best_interv matrix including probability intervention cost-effective values willingness pay parameter ceaf vector containing cost-effectiveness acceptability frontier","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness Analysis With Multiple Comparison — multi.ce","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) plot=FALSE # inhibits graphical output ) mce <- multi.ce(m) # uses the results of the economic analysis ceac.plot(mce) ceaf.plot(mce)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Multiple bcea Graphs — multiplot","title":"Plot Multiple bcea Graphs — multiplot","text":"Arrange plots grid. Sourced R graphics cookbook.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Multiple bcea Graphs — multiplot","text":"","code":"multiplot(plotlist = NULL, cols = 1, layout_config = NULL)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Multiple bcea Graphs — multiplot","text":"plotlist List ggplot objects cols Number columns layout_config Matrix plot configuration","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Multiple bcea Graphs — multiplot","text":"ggplot TableGrob object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for bcea — new_bcea","title":"Constructor for bcea — new_bcea","text":"Constructor bcea","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for bcea — new_bcea","text":"","code":"new_bcea(df_ce, k)"},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for bcea — new_bcea","text":"df_ce Dataframe simulation eff cost k Vector willingness pay values","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for bcea — new_bcea","text":"List object class bcea.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/num_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Get number of lines — num_lines","title":"Get number of lines — num_lines","text":"Get number lines","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/num_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get number of lines — num_lines","text":"","code":"num_lines(dat) # S3 method for pairwise num_lines(dat) # S3 method for bcea num_lines(dat) # S3 method for evppi num_lines(dat) # S3 method for default num_lines(dat)"},{"path":"https://n8thangreen.github.io/BCEA/reference/num_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get number of lines — num_lines","text":"dat Data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/openPDF.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatically open pdf output using default pdf viewer — openPDF","title":"Automatically open pdf output using default pdf viewer — openPDF","text":"Automatically open pdf output using default pdf viewer","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/openPDF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatically open pdf output using default pdf viewer — openPDF","text":"","code":"openPDF(file_name)"},{"path":"https://n8thangreen.github.io/BCEA/reference/openPDF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatically open pdf output using default pdf viewer — openPDF","text":"file_name String file names pdf","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Plot of the Health Economic Analysis — plot.bcea","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"Plots single graph Cost-Effectiveness plane, Expected Incremental Benefit, CEAC EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"","code":"# S3 method for bcea plot( x, comparison = NULL, wtp = 25000, pos = FALSE, graph = c(\"base\", \"ggplot2\"), ... )"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"x bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). wtp value willingness pay parameter. passed ceplane.plot(). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods ceplane.plot() eib.plot(). Please see manual pages individual functions. Arguments like size, ICER.size plot.cri can supplied functions way. addition graph=\"ggplot2\" arguments named theme objects added plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"plot four graphical summaries health economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"default position legend cost-effectiveness plane (produced ceplane.plot()) set c(1, 1.025) overriding default pos=FALSE, since multiple ggplot2 plots rendered slightly different way single plots.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Plot of the Health Economic Analysis — plot.bcea","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA he <- bcea( e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) plot=FALSE # does not produce graphical outputs ) # Plots the summary plots for the \"bcea\" object m using base graphics plot(he, graph = \"base\") # Plots the same summary plots using ggplot2 if(require(ggplot2)){ plot(he, graph = \"ggplot2\") ##### Example of a customized plot.bcea with ggplot2 plot(he, graph = \"ggplot2\", # use ggplot2 theme = theme(plot.title=element_text(size=rel(1.25))), # theme elements must have a name ICER_size = 1.5, # hidden option in ceplane.plot size = rel(2.5) # modifies the size of k = labels ) # in ceplane.plot and eib.plot }"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"Summary plot health economic analysis risk aversion included.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"","code":"# S3 method for CEriskav plot(x, pos = c(0, 1), graph = c(\"base\", \"ggplot2\"), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"x object class CEriskav, subclass bcea, containing results economic analysis performed accounting risk aversion parameter (obtained output function CEriskav()). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods, graphical parameters (see par()).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"list(eib,evi) two-elements named list ggplot objects containing requested plots. Returned graph=\"ggplot2\". function produces two plots risk aversion analysis. first one EIB function discrete grid approximation willingness parameter possible values risk aversion parameter, r. second one similar plot EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"Plots Expected Incremental Benefit Expected Value Perfect Information risk aversion included utility function.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots EIB and EVPI for the Risk Aversion Case — plot.CEriskav","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # # Load the processed results of the MCMC simulation model data(Vaccine) # # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) plot=FALSE # inhibits graphical output ) # # Define the vector of values for the risk aversion parameter, r, eg: r <- c(1e-10, 0.005, 0.020, 0.035) # # Run the cost-effectiveness analysis accounting for risk aversion # \\donttest{ CEriskav(m) <- r # } # # produce the plots # \\donttest{ plot(m) # } ## Alternative options, using ggplot2 # \\donttest{ plot(m, graph = \"ggplot2\") # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"Plot Expected Value Partial Information Respect Set Parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"","code":"# S3 method for evppi plot(x, pos = c(0, 0.8), graph = c(\"base\", \"ggplot2\"), col = c(1, 1), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"x object class evppi, obtained call function evppi(). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". col Sets colour lines depicted graph. ... Arguments passed methods, graphical parameters (see par()).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"Plot base R ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters — plot.evppi","text":"","code":"if (FALSE) { data(Vaccine, package = \"BCEA\") treats <- c(\"Status quo\", \"Vaccination\") # Run the health economic evaluation using BCEA m <- bcea(e.pts, c.pts, ref = 2, interventions = treats) # Compute the EVPPI for a bunch of parameters inp <- createInputs(vaccine_mat) # Compute the EVPPI using INLA/SPDE if (require(\"INLA\")) { x0 <- evppi(m, c(\"beta.1.\" , \"beta.2.\"), input = inp$mat) plot(x0, pos = c(0,1)) x1 <- evppi(m, c(32,48,49), input = inp$mat) plot(x1, pos = \"topright\") plot(x0, col = c(\"black\", \"red\"), pos = \"topright\") plot(x0, col = c(2,3), pos = \"bottomright\") plot(x0, pos = c(0,1), graph = \"ggplot2\") plot(x1, pos = \"top\", graph = \"ggplot2\") plot(x0, col = c(\"black\", \"red\"), pos = \"right\", graph = \"ggplot2\") plot(x0, col = c(2,3), size = c(1,2), pos = \"bottom\", graph = \"ggplot2\") plot(x0, graph = \"ggplot2\", theme = ggplot2::theme_linedraw()) } if (FALSE) plot(x0, col = 3, pos = \"topright\") # The vector 'col' must have the number of elements for an EVPI # colour and each of the EVPPI parameters. Forced to black }"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"Mesh Plot — plot.mesh","title":"Mesh Plot — plot.mesh","text":"Option interactively saving plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mesh Plot — plot.mesh","text":"","code":"# S3 method for mesh plot(mesh, data, plot)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mesh Plot — plot.mesh","text":"mesh Mesh data Data plot Create plot? logical","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_eib_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Credible Intervals — plot_eib_cri","title":"Plot Credible Intervals — plot_eib_cri","text":"Bayesian posterior credible intervals willingness pay.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_eib_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Credible Intervals — plot_eib_cri","text":"","code":"plot_eib_cri(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_eib_cri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Credible Intervals — plot_eib_cri","text":"bcea object containing results Bayesian modelling economic evaluation. params Graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"Mesh Plot — plot_mesh","title":"Mesh Plot — plot_mesh","text":"Option interactively saving plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mesh Plot — plot_mesh","text":"","code":"plot_mesh(mesh, data, plot, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mesh Plot — plot_mesh","text":"mesh Mesh data Data plot Create plot? logical ... Additional parameters","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/post.density.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Process Fitting — post.density","title":"Gaussian Process Fitting — post.density","text":"Gaussian Process Fitting","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/post.density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Process Fitting — post.density","text":"","code":"post.density(hyperparams, parameter, x, input.matrix)"},{"path":"https://n8thangreen.github.io/BCEA/reference/post.density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Process Fitting — post.density","text":"hyperparams Hyperparameters parameter Parameters x Response variable input.matrix Input data matrix","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/prep.x.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Delta arrays — prep.x","title":"Prepare Delta arrays — prep.x","text":"Prepare Delta arrays","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep.x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Delta arrays — prep.x","text":"","code":"prep.x(he, seq_rows, k, l)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep.x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Delta arrays — prep.x","text":"bcea object containing results Bayesian modelling economic evaluation. seq_rows Rows (e,c) keep k e c? 1 2. l Columns (e,c) keep","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare output — prepare.output","title":"Prepare output — prepare.output","text":"Prepare output","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare output — prepare.output","text":"","code":"prepare.output(parameters, inputs)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare output — prepare.output","text":"parameters Parameters inputs Inputs","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare output — prepare.output","text":"name","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare CE-plane Parameters — prep_ceplane_params","title":"Prepare CE-plane Parameters — prep_ceplane_params","text":"ggplot format, combine user-supplied parameters defaults.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare CE-plane Parameters — prep_ceplane_params","text":"","code":"prep_ceplane_params(he, wtp, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare CE-plane Parameters — prep_ceplane_params","text":"bcea object containing results Bayesian modelling economic evaluation. wtp Willingness--pay ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare CE-plane Parameters — prep_ceplane_params","text":"List pf graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare EIB plot parameters — prep_eib_params","title":"Prepare EIB plot parameters — prep_eib_params","text":"Parameters general plotting devices.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare EIB plot parameters — prep_eib_params","text":"","code":"prep_eib_params(he, plot.cri, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare EIB plot parameters — prep_eib_params","text":"bcea object containing results Bayesian modelling economic evaluation. plot.cri Make title including credible interval? Logical ... Additional parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare EIB plot parameters — prep_eib_params","text":"List graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare frontier data — prep_frontier_data","title":"Prepare frontier data — prep_frontier_data","text":"Prepare frontier data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare frontier data — prep_frontier_data","text":"","code":"prep_frontier_data(he, threshold = NULL, start.origin = TRUE)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare frontier data — prep_frontier_data","text":"bcea object containing results Bayesian modelling economic evaluation. threshold Cost-effectiveness threshold .e angle line. Must >=0 NULL. start.origin frontier start ?","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare frontier data — prep_frontier_data","text":"List scatter.data, ceef.points, orig.avg","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"bcea Print Method — print.bcea","title":"bcea Print Method — print.bcea","text":"bcea Print Method","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bcea Print Method — print.bcea","text":"","code":"# S3 method for bcea print(x, digits = getOption(\"digits\"), give.attr = FALSE, no.list = TRUE, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"bcea Print Method — print.bcea","text":"x bcea object containing results Bayesian modelling economic evaluation. digits Minimal number significant digits, see print.default(). give.attr Logical; TRUE (default), show attributes sub structures. .list Logical; TRUE, ‘list ...’ class printed. ... Potential arguments.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"bcea Print Method — print.bcea","text":"","code":"data(\"Vaccine\") he <- BCEA::bcea(eff, cost) #> No reference selected. Defaulting to first intervention."},{"path":"https://n8thangreen.github.io/BCEA/reference/quadrant_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Quadrant Parameters\r\nrequires just a single comparison group — quadrant_params","title":"Quadrant Parameters\r\nrequires just a single comparison group — quadrant_params","text":"Quadrant Parameters requires just single comparison group","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/quadrant_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quadrant Parameters\r\nrequires just a single comparison group — quadrant_params","text":"","code":"quadrant_params(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/quiet.html","id":null,"dir":"Reference","previous_headings":"","what":"Allow disabling of the cat messages — quiet","title":"Allow disabling of the cat messages — quiet","text":"Allow disabling cat messages","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/quiet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allow disabling of the cat messages — quiet","text":"","code":"quiet(x)"},{"path":"https://n8thangreen.github.io/BCEA/reference/quiet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allow disabling of the cat messages — quiet","text":"x Object quietly return","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Choose Graphical Engine — select_plot_type","title":"Choose Graphical Engine — select_plot_type","text":"base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Choose Graphical Engine — select_plot_type","text":"","code":"select_plot_type(graph)"},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Choose Graphical Engine — select_plot_type","text":"graph Type names; string","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Choose Graphical Engine — select_plot_type","text":"Plot ID integer 1:base R; 2:ggplot2; 3:plotly","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Comparisons Group — setComparisons","title":"Set Comparisons Group — setComparisons","text":"One alternative way set (e,c) comparison group. Simply recompute comparisons drop unwanted.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Comparisons Group — setComparisons","text":"","code":"setComparisons(he, comparison)"},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Comparisons Group — setComparisons","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2).","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Comparison Group — setComparisons_assign","title":"Set Comparison Group — setComparisons_assign","text":"One alternative way set (e,c) comparison group.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Comparison Group — setComparisons_assign","text":"","code":"setComparisons(he) <- value # S3 method for bcea setComparisons(he) <- value # S3 method for default setComparisons(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Comparison Group — setComparisons_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value Comparison","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Comparison Group — setComparisons_assign","text":"bcea-type object","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Maximum Willingness to Pay — setKmax_assign","title":"Set Maximum Willingness to Pay — setKmax_assign","text":"Alternative way define K statistic.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Maximum Willingness to Pay — setKmax_assign","text":"","code":"setKmax(he) <- value # S3 method for bcea setKmax(he) <- value # S3 method for default setKmax(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Maximum Willingness to Pay — setKmax_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value Maximum willingness pay","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Maximum Willingness to Pay — setKmax_assign","text":"bcea-type object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Reference Group — setReferenceGroup_assign","title":"Set Reference Group — setReferenceGroup_assign","text":"Alternative way define (e,c) reference group.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Reference Group — setReferenceGroup_assign","text":"","code":"setReferenceGroup(he) <- value # S3 method for bcea setReferenceGroup(he) <- value # S3 method for default setReferenceGroup(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Reference Group — setReferenceGroup_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value Reference group number","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Reference Group — setReferenceGroup_assign","text":"bcea-type object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Table of Simulation Statistics for the Health Economic Model — sim_table","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"Using input form MCMC simulations run health economic model, produces summary table simulations cost-effectiveness analysis.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"","code":"sim_table(he, ...) # S3 method for bcea sim_table(he, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"bcea object containing results Bayesian modelling economic evaluation. ... Additional arguments wtp value willingness pay threshold used summary table.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"Produces following elements: table table simulation statistics economic model names.cols vector labels associated column table wtp selected value willingness pay idx_wtp index associated selected value willingness pay threshold grid used run analysis","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table of Simulation Statistics for the Health Economic Model — sim_table","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff, # defines the variables of c=cost, # effectiveness and cost ref=2, # selects the 2nd row of (e, c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000) # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0, Kmax) # Now can save the simulation exercise in an object using sim_table() sim_table(m, # uses the results of the economic evaluation wtp=25000) # selects the particular value for k #> $Table #> U1 U2 U* IB2_1 OL VI #> 1 -36.575816 -38.71760 -36.575816 -2.141786568 2.14178657 -1.75012057 #> 2 -27.925136 -27.67448 -27.674479 0.250657304 0.00000000 7.15121672 #> 3 -28.030244 -33.37394 -28.030244 -5.343696338 5.34369634 6.79545099 #> 4 -53.284080 -47.13734 -47.137342 6.146738369 0.00000000 -12.31164646 #> 5 -43.583889 -40.40469 -40.404691 3.179197609 0.00000000 -5.57899569 #> 6 -42.374564 -33.08547 -33.085465 9.289098731 0.00000000 1.74023011 #> 7 -32.442356 -37.50684 -32.442356 -5.064481574 5.06448157 2.38333915 #> 8 -51.938478 -44.82658 -44.826583 7.111894853 0.00000000 -10.00088760 #> 9 -24.636412 -23.98616 -23.986156 0.650256008 0.00000000 10.83953966 #> 10 -46.181241 -37.34967 -37.349674 8.831567129 0.00000000 -2.52397818 #> 11 -58.095859 -45.57886 -45.578863 12.516995944 0.00000000 -10.75316756 #> 12 -32.026167 -30.02358 -30.023579 2.002587855 0.00000000 4.80211644 #> 13 -24.890578 -26.54063 -24.890578 -1.650056731 1.65005673 9.93511782 #> 14 -51.083390 -34.23447 -34.234473 16.848916771 0.00000000 0.59122249 #> 15 -70.975370 -50.83694 -50.836939 20.138431719 0.00000000 -16.01124327 #> 16 -32.343576 -35.74223 -32.343576 -3.398653900 3.39865390 2.48211897 #> 17 -11.337758 -16.12295 -11.337758 -4.785191763 4.78519176 23.48793764 #> 18 -39.751995 -29.86185 -29.861855 9.890139925 0.00000000 4.96384036 #> 19 -22.209179 -20.04740 -20.047398 2.161781794 0.00000000 14.77829781 #> 20 -45.634345 -62.35286 -45.634345 -16.718519479 16.71851948 -10.80864921 #> 21 -57.546460 -58.46125 -57.546460 -0.914786605 0.91478660 -22.72076498 #> 22 -55.815147 -47.81379 -47.813791 8.001355368 0.00000000 -12.98809588 #> 23 -65.979134 -66.06333 -65.979134 -0.084192580 0.08419258 -31.15343861 #> 24 -15.835575 -26.56841 -15.835575 -10.732836457 10.73283646 18.99012037 #> 25 -21.058746 -35.30491 -21.058746 -14.246165005 14.24616500 13.76694955 #> 26 -60.520260 -42.14622 -42.146217 18.374042590 0.00000000 -7.32052195 #> 27 -28.205540 -35.51466 -28.205540 -7.309120295 7.30912030 6.62015527 #> 28 -32.546793 -35.80048 -32.546793 -3.253683844 3.25368384 2.27890260 #> 29 -16.380093 -21.60875 -16.380093 -5.228658088 5.22865809 18.44560236 #> 30 -13.457847 -14.79877 -13.457847 -1.340925281 1.34092528 21.36784849 #> 31 -65.583149 -52.36537 -52.365370 13.217778975 0.00000000 -17.53967468 #> 32 -17.028951 -19.81625 -17.028951 -2.787299396 2.78729940 17.79674468 #> 33 -26.907486 -24.45174 -24.451744 2.455741729 0.00000000 10.37395142 #> 34 -31.516522 -28.31237 -28.312370 3.204152450 0.00000000 6.51332535 #> 35 -30.040100 -30.98166 -30.040100 -0.941562516 0.94156252 4.78559531 #> 36 -41.829563 -34.43487 -34.434870 7.394693338 0.00000000 0.39082575 #> 37 -47.162775 -40.30695 -40.306946 6.855828925 0.00000000 -5.48125071 #> 38 -23.397917 -36.38016 -23.397917 -12.982237974 12.98223797 11.42777809 #> 39 -45.966264 -42.40697 -42.406965 3.559298695 0.00000000 -7.58127000 #> 40 -30.728312 -33.35439 -30.728312 -2.626079843 2.62607984 4.09738320 #> 41 -50.290582 -46.21712 -46.217119 4.073462811 0.00000000 -11.39142371 #> 42 -21.747744 -29.78883 -21.747744 -8.041085884 8.04108588 13.07795164 #> 43 -36.740928 -30.35715 -30.357146 6.383781601 0.00000000 4.46854936 #> 44 -24.905508 -39.28508 -24.905508 -14.379574532 14.37957453 9.92018707 #> 45 -53.629450 -39.94290 -39.942903 13.686546690 0.00000000 -5.11720771 #> 46 -32.731200 -28.45269 -28.452689 4.278510704 0.00000000 6.37300616 #> 47 -38.813444 -37.71039 -37.710393 1.103050871 0.00000000 -2.88469761 #> 48 -60.413052 -46.71309 -46.713085 13.699966967 0.00000000 -11.88738988 #> 49 -21.078197 -19.70921 -19.709211 1.368985396 0.00000000 15.11648388 #> 50 -31.283131 -32.32021 -31.283131 -1.037080196 1.03708020 3.54256394 #> 51 -32.450047 -41.29502 -32.450047 -8.844978195 8.84497820 2.37564867 #> 52 -53.773562 -50.06956 -50.069563 3.703998418 0.00000000 -15.24386791 #> 53 -39.256315 -31.63766 -31.637663 7.618651922 0.00000000 3.18803237 #> 54 -32.095946 -28.83281 -28.832811 3.263135708 0.00000000 5.99288467 #> 55 -41.251946 -44.26889 -41.251946 -3.016945048 3.01694505 -6.42625028 #> 56 -56.130175 -46.32287 -46.322871 9.807304205 0.00000000 -11.49717582 #> 57 -23.751435 -41.63394 -23.751435 -17.882509187 17.88250919 11.07426052 #> 58 -36.168239 -29.83677 -29.836770 6.331468540 0.00000000 4.98892489 #> 59 -31.311610 -31.06406 -31.064060 0.247550071 0.00000000 3.76163553 #> 60 -57.964741 -44.05080 -44.050797 13.913943492 0.00000000 -9.22510211 #> 61 -42.581161 -36.25501 -36.255012 6.326148865 0.00000000 -1.42931660 #> 62 -39.898754 -29.85403 -29.854033 10.044721225 0.00000000 4.97166211 #> 63 -53.781040 -44.48163 -44.481631 9.299409087 0.00000000 -9.65593570 #> 64 -69.614271 -48.36707 -48.367068 21.247202691 0.00000000 -13.54137270 #> 65 -16.468131 -25.18588 -16.468131 -8.717744390 8.71774439 18.35756408 #> 66 -19.604551 -26.60639 -19.604551 -7.001836291 7.00183629 15.22114415 #> 67 -45.405402 -43.17445 -43.174446 2.230955572 0.00000000 -8.34875062 #> 68 -26.864505 -27.54852 -26.864505 -0.684019271 0.68401927 7.96119020 #> 69 -37.607518 -36.44240 -36.442401 1.165116878 0.00000000 -1.61670555 #> 70 -38.487461 -41.94032 -38.487461 -3.452862170 3.45286217 -3.66176588 #> 71 -23.027365 -25.09422 -23.027365 -2.066858536 2.06685854 11.79833017 #> 72 -14.336558 -18.17653 -14.336558 -3.839968809 3.83996881 20.48913714 #> 73 -45.763256 -37.42407 -37.424070 8.339185994 0.00000000 -2.59837477 #> 74 -38.293169 -38.54554 -38.293169 -0.252373112 0.25237311 -3.46747371 #> 75 -50.172148 -47.85474 -47.854736 2.317412007 0.00000000 -13.02904027 #> 76 -23.809870 -22.01309 -22.013093 1.796777165 0.00000000 12.81260243 #> 77 -37.748501 -30.80923 -30.809229 6.939271892 0.00000000 4.01646583 #> 78 -23.524954 -27.07055 -23.524954 -3.545599930 3.54559993 11.30074119 #> 79 -47.140215 -32.83611 -32.836111 14.304104346 0.00000000 1.98958439 #> 80 -25.063105 -24.82709 -24.827085 0.236019793 0.00000000 9.99860992 #> 81 -37.676658 -35.71838 -35.718380 1.958278380 0.00000000 -0.89268464 #> 82 -27.961527 -37.20587 -27.961527 -9.244344164 9.24434416 6.86416789 #> 83 -26.488039 -32.34358 -26.488039 -5.855537156 5.85553716 8.33765678 #> 84 -17.818883 -28.87161 -17.818883 -11.052727716 11.05272772 17.00681255 #> 85 -39.009508 -35.86738 -35.867378 3.142130329 0.00000000 -1.04168258 #> 86 -38.533784 -31.32709 -31.327093 7.206691469 0.00000000 3.49860255 #> 87 -36.486330 -27.86485 -27.864848 8.621482106 0.00000000 6.96084695 #> 88 -59.789430 -57.11228 -57.112285 2.677145466 0.00000000 -22.28658919 #> 89 -24.151853 -26.45561 -24.151853 -2.303754714 2.30375471 10.67384233 #> 90 -16.370847 -19.69877 -16.370847 -3.327921974 3.32792197 18.45484816 #> 91 -29.632758 -29.51358 -29.513584 0.119174302 0.00000000 5.31211156 #> 92 -39.893569 -39.49942 -39.499421 0.394148010 0.00000000 -4.67372527 #> 93 -21.272298 -33.24018 -21.272298 -11.967880869 11.96788087 13.55339705 #> 94 -30.387725 -37.05681 -30.387725 -6.669085502 6.66908550 4.43797030 #> 95 -33.350816 -31.41187 -31.411871 1.938944615 0.00000000 3.41382385 #> 96 -40.029505 -35.15516 -35.155161 4.874343446 0.00000000 -0.32946605 #> 97 -17.702393 -26.73252 -17.702393 -9.030124646 9.03012465 17.12330254 #> 98 -16.127988 -24.72537 -16.127988 -8.597384356 8.59738436 18.69770758 #> 99 -50.581388 -43.91244 -43.912443 6.668944785 0.00000000 -9.08674742 #> 100 -64.221127 -53.02862 -53.028617 11.192510294 0.00000000 -18.20292172 #> 101 -36.130258 -35.79804 -35.798040 0.332217564 0.00000000 -0.97234507 #> 102 -35.362101 -34.25108 -34.251078 1.111022605 0.00000000 0.57461703 #> 103 -35.294245 -30.87344 -30.873443 4.420801341 0.00000000 3.95225210 #> 104 -57.272375 -38.28796 -38.287956 18.984419500 0.00000000 -3.46226020 #> 105 -32.479352 -25.94254 -25.942536 6.536816830 0.00000000 8.88315967 #> 106 -39.643964 -32.67332 -32.673319 6.970644816 0.00000000 2.15237657 #> 107 -31.195435 -30.59790 -30.597905 0.597530351 0.00000000 4.22779068 #> 108 -75.368749 -68.87114 -68.871141 6.497608011 0.00000000 -34.04544564 #> 109 -61.750837 -40.70930 -40.709297 21.041539633 0.00000000 -5.88360202 #> 110 -20.317452 -29.21184 -20.317452 -8.894387577 8.89438758 14.50824370 #> 111 -27.512964 -33.35347 -27.512964 -5.840509056 5.84050906 7.31273132 #> 112 -36.481115 -33.36829 -33.368290 3.112824813 0.00000000 1.45740490 #> 113 -32.820145 -38.43081 -32.820145 -5.610661383 5.61066138 2.00555042 #> 114 -36.545720 -41.35659 -36.545720 -4.810874470 4.81087447 -1.72002482 #> 115 -32.686484 -32.78388 -32.686484 -0.097400906 0.09740091 2.13921135 #> 116 -23.515710 -25.64790 -23.515710 -2.132185386 2.13218539 11.30998493 #> 117 -25.267368 -26.91231 -25.267368 -1.644946781 1.64494678 9.55832750 #> 118 -37.768277 -36.61777 -36.617767 1.150510161 0.00000000 -1.79207194 #> 119 -26.373342 -25.00879 -25.008792 1.364550375 0.00000000 9.81690373 #> 120 -29.410483 -27.68329 -27.683286 1.727196759 0.00000000 7.14240912 #> 121 -18.101748 -21.30367 -18.101748 -3.201923502 3.20192350 16.72394732 #> 122 -38.641283 -32.76287 -32.762875 5.878408065 0.00000000 2.06282036 #> 123 -33.765743 -44.97225 -33.765743 -11.206504323 11.20650432 1.05995245 #> 124 -23.770044 -32.16060 -23.770044 -8.390560525 8.39056052 11.05565098 #> 125 -30.701205 -40.14649 -30.701205 -9.445283451 9.44528345 4.12449063 #> 126 -50.582718 -46.21238 -46.212379 4.370339514 0.00000000 -11.38668364 #> 127 -20.976377 -22.33520 -20.976377 -1.358827239 1.35882724 13.84931785 #> 128 -22.305728 -22.21149 -22.211487 0.094241483 0.00000000 12.61420841 #> 129 -21.095577 -29.42030 -21.095577 -8.324721461 8.32472146 13.73011804 #> 130 -73.165434 -60.35362 -60.353618 12.811816080 0.00000000 -25.52792240 #> 131 -14.167808 -17.44756 -14.167808 -3.279747387 3.27974739 20.65788730 #> 132 -35.898002 -27.55970 -27.559703 8.338299594 0.00000000 7.26599255 #> 133 -40.270884 -39.93264 -39.932643 0.338240439 0.00000000 -5.10694782 #> 134 -38.734717 -41.17318 -38.734717 -2.438466452 2.43846645 -3.90902136 #> 135 -29.156116 -30.85489 -29.156116 -1.698769769 1.69876977 5.66957895 #> 136 -22.955637 -21.33305 -21.333052 1.622584124 0.00000000 13.49264284 #> 137 -62.252900 -42.70410 -42.704101 19.548799049 0.00000000 -7.87840533 #> 138 -102.177758 -98.97074 -98.970738 3.207020713 0.00000000 -64.14504225 #> 139 -32.796649 -32.64367 -32.643667 0.152981896 0.00000000 2.18202826 #> 140 -47.678110 -33.08435 -33.084353 14.593756554 0.00000000 1.74134216 #> 141 -21.151776 -25.22621 -21.151776 -4.074438158 4.07443816 13.67391883 #> 142 -32.122050 -30.08375 -30.083752 2.038298186 0.00000000 4.74194333 #> 143 -26.296902 -21.54334 -21.543335 4.753566466 0.00000000 13.28235983 #> 144 -43.025423 -36.88249 -36.882494 6.142929215 0.00000000 -2.05679820 #> 145 -42.776739 -36.51205 -36.512047 6.264691852 0.00000000 -1.68635164 #> 146 -37.569702 -36.68284 -36.682845 0.886856848 0.00000000 -1.85714960 #> 147 -24.209486 -27.40378 -24.209486 -3.194292535 3.19429253 10.61620946 #> 148 -47.982241 -48.38364 -47.982241 -0.401395621 0.40139562 -13.15654598 #> 149 -77.713228 -40.99638 -40.996379 36.716849222 0.00000000 -6.17068381 #> 150 -43.769330 -42.28314 -42.283136 1.486194422 0.00000000 -7.45744060 #> 151 -34.694712 -34.31585 -34.315852 0.378859966 0.00000000 0.50984346 #> 152 -41.453318 -36.63997 -36.639973 4.813345061 0.00000000 -1.81427779 #> 153 -16.005288 -31.02543 -16.005288 -15.020139478 15.02013948 18.82040685 #> 154 -25.713444 -28.85638 -25.713444 -3.142937387 3.14293739 9.11225157 #> 155 -37.226397 -35.77600 -35.776001 1.450396149 0.00000000 -0.95030597 #> 156 -55.145835 -48.32931 -48.329315 6.816520307 0.00000000 -13.50361959 #> 157 -32.063611 -34.97150 -32.063611 -2.907888768 2.90788877 2.76208425 #> 158 -26.914447 -27.30613 -26.914447 -0.391679010 0.39167901 7.91124801 #> 159 -30.465354 -34.34856 -30.465354 -3.883208099 3.88320810 4.36034114 #> 160 -17.128446 -22.01007 -17.128446 -4.881623036 4.88162304 17.69724973 #> 161 -18.982632 -21.73387 -18.982632 -2.751241073 2.75124107 15.84306340 #> 162 -29.627395 -31.66658 -29.627395 -2.039187271 2.03918727 5.19829996 #> 163 -48.904341 -46.09079 -46.090787 2.813553833 0.00000000 -11.26509137 #> 164 -18.961366 -37.81434 -18.961366 -18.852976457 18.85297646 15.86432938 #> 165 -54.053516 -40.70611 -40.706107 13.347409462 0.00000000 -5.88041128 #> 166 -23.368485 -27.95576 -23.368485 -4.587274212 4.58727421 11.45721065 #> 167 -68.408452 -55.10636 -55.106361 13.302090755 0.00000000 -20.28066590 #> 168 -14.396777 -19.08608 -14.396777 -4.689303738 4.68930374 20.42891833 #> 169 -21.877446 -22.32783 -21.877446 -0.450385931 0.45038593 12.94824965 #> 170 -52.088647 -39.23704 -39.237038 12.851609740 0.00000000 -4.41134223 #> 171 -40.602559 -38.00148 -38.001479 2.601079055 0.00000000 -3.17578416 #> 172 -90.117895 -64.17205 -64.172054 25.945841723 0.00000000 -29.34635819 #> 173 -42.967861 -41.29128 -41.291278 1.676582647 0.00000000 -6.46558312 #> 174 -18.919770 -24.94893 -18.919770 -6.029161507 6.02916151 15.90592528 #> 175 -27.905601 -24.48260 -24.482599 3.423001841 0.00000000 10.34309617 #> 176 -21.678387 -28.27715 -21.678387 -6.598765388 6.59876539 13.14730854 #> 177 -50.563328 -41.19976 -41.199759 9.363569435 0.00000000 -6.37406371 #> 178 -41.250746 -31.43356 -31.433562 9.817184236 0.00000000 3.39213366 #> 179 -26.031496 -22.41968 -22.419679 3.611816799 0.00000000 12.40601593 #> 180 -38.494198 -29.46339 -29.463386 9.030811584 0.00000000 5.36230906 #> 181 -48.839379 -48.48224 -48.482242 0.357137333 0.00000000 -13.65654643 #> 182 -56.157495 -47.01324 -47.013237 9.144257614 0.00000000 -12.18754196 #> 183 -33.728531 -28.12095 -28.120955 5.607576517 0.00000000 6.70474063 #> 184 -39.189497 -27.14658 -27.146580 12.042916152 0.00000000 7.67911492 #> 185 -31.813690 -25.48912 -25.489124 6.324565778 0.00000000 9.33657110 #> 186 -31.362853 -28.05437 -28.054367 3.308485529 0.00000000 6.77132829 #> 187 -43.777680 -38.65037 -38.650366 5.127314321 0.00000000 -3.82467024 #> 188 -57.779115 -37.64703 -37.647026 20.132089505 0.00000000 -2.82133030 #> 189 -30.089107 -27.57487 -27.574865 2.514241541 0.00000000 7.25083024 #> 190 -39.009576 -36.26101 -36.261014 2.748562204 0.00000000 -1.43531850 #> 191 -61.747320 -57.25016 -57.250158 4.497161323 0.00000000 -22.42446314 #> 192 -18.906172 -18.93203 -18.906172 -0.025862055 0.02586206 15.91952330 #> 193 -56.192418 -54.60127 -54.601272 1.591146473 0.00000000 -19.77557663 #> 194 -19.913740 -21.94147 -19.913740 -2.027729431 2.02772943 14.91195549 #> 195 -58.460399 -46.11007 -46.110069 12.350329984 0.00000000 -11.28437403 #> 196 -34.404608 -38.74589 -34.404608 -4.341280845 4.34128084 0.42108742 #> 197 -23.395499 -27.07562 -23.395499 -3.680120680 3.68012068 11.43019646 #> 198 -34.999742 -27.90102 -27.901016 7.098726119 0.00000000 6.92467946 #> 199 -37.690703 -39.77269 -37.690703 -2.081983179 2.08198318 -2.86500719 #> 200 -33.017476 -31.09734 -31.097345 1.920130630 0.00000000 3.72835039 #> 201 -16.911293 -23.02700 -16.911293 -6.115703832 6.11570383 17.91440223 #> 202 -19.342889 -23.85679 -19.342889 -4.513896800 4.51389680 15.48280598 #> 203 -27.260617 -29.87794 -27.260617 -2.617319288 2.61731929 7.56507813 #> 204 -22.972979 -21.96643 -21.966434 1.006544777 0.00000000 12.85926137 #> 205 -41.258759 -34.07300 -34.073004 7.185755468 0.00000000 0.75269171 #> 206 -30.176787 -36.90721 -30.176787 -6.730420547 6.73042055 4.64890881 #> 207 -31.819854 -36.28683 -31.819854 -4.466981046 4.46698105 3.00584182 #> 208 -15.929191 -22.93669 -15.929191 -7.007494219 7.00749422 18.89650396 #> 209 -38.234151 -38.41219 -38.234151 -0.178038501 0.17803850 -3.40845615 #> 210 -62.016744 -61.40633 -61.406325 0.610418345 0.00000000 -26.58062991 #> 211 -30.920267 -37.56909 -30.920267 -6.648825085 6.64882509 3.90542868 #> 212 -25.915982 -24.11569 -24.115694 1.800287779 0.00000000 10.71000132 #> 213 -23.821314 -33.97603 -23.821314 -10.154715820 10.15471582 11.00438179 #> 214 -13.685559 -17.78091 -13.685559 -4.095351514 4.09535151 21.14013606 #> 215 -61.026306 -55.22339 -55.223392 5.802913499 0.00000000 -20.39769707 #> 216 -27.915241 -35.54518 -27.915241 -7.629944317 7.62994432 6.91045482 #> 217 -27.912598 -24.57577 -24.575766 3.336831905 0.00000000 10.24992953 #> 218 -29.361519 -25.46925 -25.469246 3.892273301 0.00000000 9.35644952 #> 219 -12.601455 -22.99872 -12.601455 -10.397261736 10.39726174 22.22424077 #> 220 -16.385197 -24.37039 -16.385197 -7.985191325 7.98519132 18.44049820 #> 221 -16.073785 -18.56594 -16.073785 -2.492153481 2.49215348 18.75190986 #> 222 -10.511486 -15.03723 -10.511486 -4.525748435 4.52574844 24.31420906 #> 223 -36.407683 -27.70955 -27.709553 8.698129977 0.00000000 7.11614220 #> 224 -41.151571 -35.44398 -35.443981 5.707590081 0.00000000 -0.61828606 #> 225 -29.700500 -28.73529 -28.735287 0.965212388 0.00000000 6.09040790 #> 226 -19.742931 -29.96756 -19.742931 -10.224624550 10.22462455 15.08276386 #> 227 -46.338858 -49.28764 -46.338858 -2.948786348 2.94878635 -11.51316245 #> 228 -32.776804 -29.36455 -29.364546 3.412258791 0.00000000 5.46114964 #> 229 -45.075520 -42.97215 -42.972147 2.103373621 0.00000000 -8.14645149 #> 230 -34.046472 -33.24967 -33.249667 0.796805211 0.00000000 1.57602816 #> 231 -34.146439 -24.05113 -24.051131 10.095307203 0.00000000 10.77456399 #> 232 -43.595901 -42.34848 -42.348483 1.247417781 0.00000000 -7.52278809 #> 233 -35.920060 -35.71861 -35.718614 0.201446577 0.00000000 -0.89291839 #> 234 -47.450643 -47.17662 -47.176621 0.274022030 0.00000000 -12.35092529 #> 235 -50.188006 -51.37649 -50.188006 -1.188482307 1.18848231 -15.36231072 #> 236 -23.931307 -26.88720 -23.931307 -2.955896243 2.95589624 10.89438826 #> 237 -52.131349 -42.23503 -42.235033 9.896315871 0.00000000 -7.40933801 #> 238 -56.067014 -54.62011 -54.620112 1.446901481 0.00000000 -19.79441696 #> 239 -34.180301 -31.95235 -31.952347 2.227953995 0.00000000 2.87334799 #> 240 -25.680040 -26.74639 -25.680040 -1.066353927 1.06635393 9.14565488 #> 241 -51.606925 -42.52557 -42.525569 9.081355645 0.00000000 -7.69987417 #> 242 -61.374696 -51.85838 -51.858384 9.516311985 0.00000000 -17.03268867 #> 243 -39.649164 -39.61088 -39.610881 0.038283277 0.00000000 -4.78518541 #> 244 -36.829876 -32.59939 -32.599386 4.230490439 0.00000000 2.22630929 #> 245 -19.435898 -25.15988 -19.435898 -5.723984879 5.72398488 15.38979749 #> 246 -39.286065 -35.83978 -35.839778 3.446287062 0.00000000 -1.01408268 #> 247 -47.771150 -34.11548 -34.115483 13.655666492 0.00000000 0.71021227 #> 248 -65.590027 -56.88299 -56.882988 8.707038476 0.00000000 -22.05729271 #> 249 -30.152605 -21.74218 -21.742181 8.410423951 0.00000000 13.08351424 #> 250 -35.675897 -43.89898 -35.675897 -8.223085552 8.22308555 -0.85020199 #> 251 -36.380570 -39.16491 -36.380570 -2.784339887 2.78433989 -1.55487424 #> 252 -54.790452 -61.76705 -54.790452 -6.976597383 6.97659738 -19.96475647 #> 253 -43.974843 -40.20818 -40.208177 3.766665475 0.00000000 -5.38248200 #> 254 -28.054282 -29.91975 -28.054282 -1.865465062 1.86546506 6.77141372 #> 255 -41.701393 -37.42450 -37.424497 4.276895637 0.00000000 -2.59880216 #> 256 -42.688927 -34.12267 -34.122671 8.566255956 0.00000000 0.70302429 #> 257 -50.069982 -34.39023 -34.390228 15.679753865 0.00000000 0.43546707 #> 258 -12.441513 -16.35756 -12.441513 -3.916049188 3.91604919 22.38418275 #> 259 -36.842453 -27.35969 -27.359686 9.482767193 0.00000000 7.46600928 #> 260 -41.597584 -47.92649 -41.597584 -6.328903327 6.32890333 -6.77188870 #> 261 -20.124542 -25.20399 -20.124542 -5.079444699 5.07944470 14.70115348 #> 262 -20.024566 -25.57924 -20.024566 -5.554677596 5.55467760 14.80112898 #> 263 -41.074834 -44.20127 -41.074834 -3.126438694 3.12643869 -6.24913866 #> 264 -32.008027 -30.98090 -30.980899 1.027128054 0.00000000 3.84479635 #> 265 -74.782290 -51.80566 -51.805658 22.976631768 0.00000000 -16.97996260 #> 266 -29.614411 -29.25661 -29.256607 0.357803879 0.00000000 5.56908790 #> 267 -18.239860 -24.96996 -18.239860 -6.730099974 6.73009997 16.58583539 #> 268 -39.447020 -37.86202 -37.862022 1.584997404 0.00000000 -3.03632712 #> 269 -32.222699 -33.34726 -32.222699 -1.124558449 1.12455845 2.60299646 #> 270 -49.926383 -65.12687 -49.926383 -15.200488058 15.20048806 -15.10068795 #> 271 -20.049289 -23.54031 -20.049289 -3.491016558 3.49101656 14.77640605 #> 272 -21.018344 -28.16049 -21.018344 -7.142147881 7.14214788 13.80735176 #> 273 -23.063988 -24.53408 -23.063988 -1.470093400 1.47009340 11.76170692 #> 274 -45.332469 -36.85232 -36.852319 8.480149641 0.00000000 -2.02662382 #> 275 -22.501973 -25.81458 -22.501973 -3.312611612 3.31261161 12.32372271 #> 276 -17.259137 -21.34312 -17.259137 -4.083985427 4.08398543 17.56655831 #> 277 -64.258591 -53.43504 -53.435038 10.823552492 0.00000000 -18.60934300 #> 278 -31.883318 -33.88484 -31.883318 -2.001526607 2.00152661 2.94237747 #> 279 -40.748800 -43.27253 -40.748800 -2.523732025 2.52373202 -5.92310437 #> 280 -25.009024 -35.51786 -25.009024 -10.508837099 10.50883710 9.81667094 #> 281 -22.546125 -31.35496 -22.546125 -8.808830889 8.80883089 12.27957070 #> 282 -31.998409 -39.03063 -31.998409 -7.032219381 7.03221938 2.82728644 #> 283 -44.260399 -44.20954 -44.209535 0.050864295 0.00000000 -9.38383985 #> 284 -26.199287 -25.56317 -25.563171 0.636116743 0.00000000 9.26252477 #> 285 -59.461220 -58.10035 -58.100354 1.360865735 0.00000000 -23.27465887 #> 286 -14.259087 -17.21384 -14.259087 -2.954756122 2.95475612 20.56660792 #> 287 -33.132436 -32.33251 -32.332505 0.799930148 0.00000000 2.49318990 #> 288 -42.442961 -40.94860 -40.948604 1.494357113 0.00000000 -6.12290901 #> 289 -42.438076 -34.12373 -34.123731 8.314344832 0.00000000 0.70196443 #> 290 -46.859489 -52.73898 -46.859489 -5.879495688 5.87949569 -12.03379334 #> 291 -19.478622 -16.80279 -16.802794 2.675827412 0.00000000 18.02290103 #> 292 -18.614943 -27.32995 -18.614943 -8.715003836 8.71500384 16.21075223 #> 293 -20.389878 -24.82569 -20.389878 -4.435814335 4.43581434 14.43581704 #> 294 -29.967894 -24.74372 -24.743717 5.224177058 0.00000000 10.08197814 #> 295 -34.107373 -37.55141 -34.107373 -3.444035849 3.44403585 0.71832261 #> 296 -35.955811 -38.87673 -35.955811 -2.920915654 2.92091565 -1.13011518 #> 297 -34.015321 -29.74825 -29.748250 4.267070870 0.00000000 5.07744504 #> 298 -19.207198 -25.80459 -19.207198 -6.597394081 6.59739408 15.61849720 #> 299 -31.550028 -28.64972 -28.649724 2.900304696 0.00000000 6.17597181 #> 300 -41.950825 -35.79362 -35.793622 6.157202273 0.00000000 -0.96792716 #> 301 -52.712579 -40.65718 -40.657179 12.055400063 0.00000000 -5.83148406 #> 302 -20.811115 -23.16218 -20.811115 -2.351063402 2.35106340 14.01457994 #> 303 -28.638310 -28.22114 -28.221139 0.417171118 0.00000000 6.60455598 #> 304 -22.340743 -24.78799 -22.340743 -2.447245561 2.44724556 12.48495232 #> 305 -41.128228 -34.89209 -34.892092 6.236136116 0.00000000 -0.06639652 #> 306 -42.468376 -45.30761 -42.468376 -2.839235666 2.83923567 -7.64268018 #> 307 -22.202042 -26.91001 -22.202042 -4.707966491 4.70796649 12.62365333 #> 308 -26.107776 -27.16343 -26.107776 -1.055651991 1.05565199 8.71791941 #> 309 -32.469032 -44.21170 -32.469032 -11.742664401 11.74266440 2.35666293 #> 310 -36.250363 -27.93476 -27.934761 8.315602547 0.00000000 6.89093467 #> 311 -26.763658 -21.58826 -21.588260 5.175398431 0.00000000 13.23743556 #> 312 -24.996491 -26.19501 -24.996491 -1.198523091 1.19852309 9.82920469 #> 313 -27.690160 -25.57636 -25.576358 2.113801910 0.00000000 9.24933684 #> 314 -38.923543 -41.43007 -38.923543 -2.506523568 2.50652357 -4.09784762 #> 315 -28.741809 -31.57840 -28.741809 -2.836589206 2.83658921 6.08388590 #> 316 -29.244280 -32.08937 -29.244280 -2.845092102 2.84509210 5.58141565 #> 317 -19.496219 -23.40420 -19.496219 -3.907984137 3.90798414 15.32947677 #> 318 -22.919748 -24.63573 -22.919748 -1.715978985 1.71597898 11.90594771 #> 319 -28.403514 -39.82735 -28.403514 -11.423839350 11.42383935 6.42218151 #> 320 -43.053489 -43.17806 -43.053489 -0.124574167 0.12457417 -8.22779408 #> 321 -43.058924 -38.03332 -38.033318 5.025605587 0.00000000 -3.20762281 #> 322 -31.296193 -29.49555 -29.495549 1.800643580 0.00000000 5.33014640 #> 323 -57.015967 -56.05437 -56.054371 0.961595531 0.00000000 -21.22867606 #> 324 -50.463378 -40.83999 -40.839991 9.623386840 0.00000000 -6.01429566 #> 325 -32.891971 -27.40295 -27.402946 5.489024629 0.00000000 7.42274918 #> 326 -44.872848 -45.85009 -44.872848 -0.977243615 0.97724361 -10.04715306 #> 327 -31.603711 -37.08808 -31.603711 -5.484371642 5.48437164 3.22198460 #> 328 -50.686113 -37.78835 -37.788346 12.897766751 0.00000000 -2.96265048 #> 329 -70.509803 -59.14535 -59.145348 11.364455443 0.00000000 -24.31965248 #> 330 -31.584861 -27.75038 -27.750382 3.834479006 0.00000000 7.07531325 #> 331 -32.064159 -23.37741 -23.377405 8.686753892 0.00000000 11.44829023 #> 332 -43.120802 -35.82440 -35.824396 7.296406733 0.00000000 -0.99870040 #> 333 -24.313646 -31.25519 -24.313646 -6.941544782 6.94154478 10.51204920 #> 334 -32.396823 -29.20847 -29.208473 3.188349511 0.00000000 5.61722225 #> 335 -56.096359 -39.74006 -39.740064 16.356295123 0.00000000 -4.91436876 #> 336 -32.651208 -29.49728 -29.497283 3.153925334 0.00000000 5.32841272 #> 337 -24.059658 -25.91636 -24.059658 -1.856704225 1.85670423 10.76603722 #> 338 -33.530768 -30.65437 -30.654375 2.876392962 0.00000000 4.17132057 #> 339 -31.755923 -34.45332 -31.755923 -2.697401099 2.69740110 3.06977256 #> 340 -47.225676 -34.98134 -34.981336 12.244339439 0.00000000 -0.15564111 #> 341 -27.860413 -32.97466 -27.860413 -5.114249763 5.11424976 6.96528195 #> 342 -38.499073 -37.57375 -37.573754 0.925319176 0.00000000 -2.74805827 #> 343 -47.954119 -40.59726 -40.597258 7.356860262 0.00000000 -5.77156298 #> 344 -35.944491 -33.87800 -33.878001 2.066490141 0.00000000 0.94769468 #> 345 -33.596701 -33.91103 -33.596701 -0.314332735 0.31433273 1.22899402 #> 346 -22.280314 -26.56911 -22.280314 -4.288799504 4.28879950 12.54538156 #> 347 -39.555872 -41.54945 -39.555872 -1.993574945 1.99357495 -4.73017701 #> 348 -55.034614 -48.50193 -48.501935 6.532678848 0.00000000 -13.67623952 #> 349 -49.230797 -36.65947 -36.659466 12.571331161 0.00000000 -1.83377034 #> 350 -12.644273 -21.04204 -12.644273 -8.397770304 8.39777030 22.18142266 #> 351 -30.990696 -29.61684 -29.616842 1.373854235 0.00000000 5.20885308 #> 352 -38.940899 -39.97423 -38.940899 -1.033329411 1.03332941 -4.11520320 #> 353 -31.382038 -35.37906 -31.382038 -3.997017403 3.99701740 3.44365726 #> 354 -41.208441 -40.70267 -40.702675 0.505766361 0.00000000 -5.87697955 #> 355 -19.350505 -32.09608 -19.350505 -12.745570485 12.74557049 15.47519080 #> 356 -25.740305 -35.40820 -25.740305 -9.667899262 9.66789926 9.08539054 #> 357 -9.246346 -22.48854 -9.246346 -13.242193568 13.24219357 25.57934912 #> 358 -16.938204 -25.20375 -16.938204 -8.265544251 8.26554425 17.88749154 #> 359 -35.260784 -39.01318 -35.260784 -3.752396471 3.75239647 -0.43508874 #> 360 -62.746500 -57.59926 -57.599258 5.147242088 0.00000000 -22.77356263 #> 361 -49.778597 -33.67946 -33.679464 16.099133318 0.00000000 1.14623158 #> 362 -45.650314 -37.02326 -37.023262 8.627052008 0.00000000 -2.19756679 #> 363 -35.782719 -33.61093 -33.610927 2.171792259 0.00000000 1.21476828 #> 364 -32.287150 -29.66346 -29.663461 2.623688445 0.00000000 5.16223403 #> 365 -39.312632 -35.53679 -35.536787 3.775844792 0.00000000 -0.71109140 #> 366 -14.198326 -22.24242 -14.198326 -8.044090081 8.04409008 20.62736974 #> 367 -49.789702 -50.36333 -49.789702 -0.573627731 0.57362773 -14.96400622 #> 368 -81.765618 -58.65676 -58.656758 23.108860391 0.00000000 -23.83106243 #> 369 -55.325002 -55.97285 -55.325002 -0.647843726 0.64784373 -20.49930643 #> 370 -47.976465 -39.06683 -39.066827 8.909638469 0.00000000 -4.24113159 #> 371 -54.059432 -48.77017 -48.770172 5.289259595 0.00000000 -13.94447687 #> 372 -24.814477 -28.90409 -24.814477 -4.089611144 4.08961114 10.01121862 #> 373 -33.987011 -29.93844 -29.938443 4.048568401 0.00000000 4.88725247 #> 374 -31.932440 -27.82212 -27.822119 4.110320991 0.00000000 7.00357662 #> 375 -27.347021 -24.66487 -24.664867 2.682154158 0.00000000 10.16082877 #> 376 -50.360806 -25.64666 -25.646664 24.714142166 0.00000000 9.17903129 #> 377 -45.970278 -38.66360 -38.663601 7.306676348 0.00000000 -3.83790597 #> 378 -25.344551 -21.10959 -21.109585 4.234966186 0.00000000 13.71611002 #> 379 -28.540140 -25.40397 -25.403971 3.136169461 0.00000000 9.42172473 #> 380 -37.732509 -34.34299 -34.342989 3.389519784 0.00000000 0.48270653 #> 381 -29.206918 -37.81110 -29.206918 -8.604185443 8.60418544 5.61877702 #> 382 -23.418369 -25.85965 -23.418369 -2.441277695 2.44127770 11.40732651 #> 383 -55.028860 -47.11273 -47.112727 7.916133012 0.00000000 -12.28703158 #> 384 -23.246556 -21.37414 -21.374141 1.872415454 0.00000000 13.45155436 #> 385 -31.636229 -24.90962 -24.909618 6.726611124 0.00000000 9.91607769 #> 386 -50.167913 -29.78429 -29.784293 20.383619849 0.00000000 5.04140219 #> 387 -39.692559 -44.20035 -39.692559 -4.507787153 4.50778715 -4.86686338 #> 388 -22.346325 -30.05385 -22.346325 -7.707529047 7.70752905 12.47937068 #> 389 -16.676861 -24.41739 -16.676861 -7.740526097 7.74052610 18.14883472 #> 390 -42.717065 -43.16157 -42.717065 -0.444504585 0.44450459 -7.89136925 #> 391 -29.530537 -27.16250 -27.162499 2.368037859 0.00000000 7.66319639 #> 392 -79.031761 -57.22100 -57.220996 21.810765338 0.00000000 -22.39530035 #> 393 -19.685308 -18.21925 -18.219248 1.466059847 0.00000000 16.60644709 #> 394 -48.051896 -45.03667 -45.036672 3.015223736 0.00000000 -10.21097702 #> 395 -37.208094 -39.48890 -37.208094 -2.280803070 2.28080307 -2.38239862 #> 396 -21.430354 -26.14106 -21.430354 -4.710710084 4.71071008 13.39534092 #> 397 -25.892068 -25.14809 -25.148092 0.743976140 0.00000000 9.67760302 #> 398 -30.339236 -36.43538 -30.339236 -6.096139196 6.09613920 4.48645934 #> 399 -49.472745 -43.28537 -43.285370 6.187375057 0.00000000 -8.45967511 #> 400 -81.406187 -66.37244 -66.372443 15.033743883 0.00000000 -31.54674796 #> 401 -49.845547 -37.83498 -37.834984 12.010562979 0.00000000 -3.00928863 #> 402 -42.432483 -37.88792 -37.887921 4.544561884 0.00000000 -3.06222591 #> 403 -31.889823 -26.26984 -26.269839 5.619983608 0.00000000 8.55585627 #> 404 -36.572721 -32.39904 -32.399040 4.173681238 0.00000000 2.42665507 #> 405 -40.007454 -35.28086 -35.280859 4.726595642 0.00000000 -0.45516322 #> 406 -54.700371 -46.84058 -46.840582 7.859789199 0.00000000 -12.01488650 #> 407 -78.640668 -55.53126 -55.531261 23.109407132 0.00000000 -20.70556561 #> 408 -28.299021 -28.93950 -28.299021 -0.640483025 0.64048303 6.52667474 #> 409 -16.418955 -26.99987 -16.418955 -10.580915682 10.58091568 18.40674055 #> 410 -51.471282 -48.44679 -48.446791 3.024490666 0.00000000 -13.62109591 #> 411 -28.012699 -23.46549 -23.465491 4.547207532 0.00000000 11.36020422 #> 412 -16.616223 -18.02326 -16.616223 -1.407036011 1.40703601 18.20947269 #> 413 -29.901845 -33.98655 -29.901845 -4.084705725 4.08470572 4.92385010 #> 414 -55.402874 -38.26707 -38.267068 17.135806209 0.00000000 -3.44137225 #> 415 -57.093138 -43.13065 -43.130650 13.962488052 0.00000000 -8.30495492 #> 416 -17.373659 -20.58657 -17.373659 -3.212913826 3.21291383 17.45203646 #> 417 -27.521073 -47.86510 -27.521073 -20.344022608 20.34402261 7.30462258 #> 418 -49.127966 -46.01121 -46.011206 3.116760562 0.00000000 -11.18551046 #> 419 -52.097467 -43.33990 -43.339900 8.757566748 0.00000000 -8.51420482 #> 420 -27.902277 -25.21048 -25.210480 2.691796580 0.00000000 9.61521515 #> 421 -24.650320 -25.77015 -24.650320 -1.119827590 1.11982759 10.17537529 #> 422 -55.579027 -54.14658 -54.146575 1.432451835 0.00000000 -19.32087990 #> 423 -23.096051 -25.36241 -23.096051 -2.266360506 2.26636051 11.72964401 #> 424 -31.821541 -29.13669 -29.136692 2.684849374 0.00000000 5.68900342 #> 425 -25.450778 -26.74546 -25.450778 -1.294682921 1.29468292 9.37491726 #> 426 -25.613111 -35.20871 -25.613111 -9.595596818 9.59559682 9.21258406 #> 427 -41.511321 -39.71511 -39.715109 1.796211778 0.00000000 -4.88941393 #> 428 -33.201303 -29.11487 -29.114865 4.086438074 0.00000000 5.71083010 #> 429 -32.869316 -34.06579 -32.869316 -1.196477566 1.19647757 1.95637910 #> 430 -57.469810 -58.10366 -57.469810 -0.633854714 0.63385471 -22.64411479 #> 431 -26.058407 -27.81994 -26.058407 -1.761536995 1.76153699 8.76728819 #> 432 -40.502840 -34.95145 -34.951446 5.551394071 0.00000000 -0.12575042 #> 433 -31.553229 -26.06939 -26.069391 5.483838019 0.00000000 8.75630441 #> 434 -19.950067 -21.08426 -19.950067 -1.134196530 1.13419653 14.87562789 #> 435 -14.710720 -26.01829 -14.710720 -11.307565839 11.30756584 20.11497557 #> 436 -32.903108 -32.23274 -32.232740 0.670367887 0.00000000 2.59295535 #> 437 -20.954125 -22.47452 -20.954125 -1.520399179 1.52039918 13.87157009 #> 438 -11.149409 -16.39406 -11.149409 -5.244653723 5.24465372 23.67628672 #> 439 -17.252369 -20.04964 -17.252369 -2.797267692 2.79726769 17.57332641 #> 440 -62.760848 -46.73360 -46.733598 16.027250661 0.00000000 -11.90790239 #> 441 -21.800120 -30.13001 -21.800120 -8.329888857 8.32988886 13.02557577 #> 442 -95.389259 -76.24788 -76.247879 19.141379825 0.00000000 -41.42218380 #> 443 -55.712193 -52.20574 -52.205744 3.506448211 0.00000000 -17.38004905 #> 444 -55.178720 -45.73175 -45.731750 9.446970087 0.00000000 -10.90605468 #> 445 -32.839359 -28.89622 -28.896221 3.943137637 0.00000000 5.92947386 #> 446 -24.649549 -25.26327 -24.649549 -0.613723443 0.61372344 10.17614653 #> 447 -36.971162 -23.88754 -23.887538 13.083624109 0.00000000 10.93815724 #> 448 -29.037690 -28.67895 -28.678947 0.358742647 0.00000000 6.14674814 #> 449 -40.832560 -27.71767 -27.717672 13.114887551 0.00000000 7.10802291 #> 450 -29.636855 -25.87993 -25.879925 3.756929283 0.00000000 8.94576984 #> 451 -36.305597 -44.01786 -36.305597 -7.712263059 7.71226306 -1.47990135 #> 452 -30.596616 -24.14990 -24.149899 6.446717220 0.00000000 10.67579625 #> 453 -19.958204 -19.83291 -19.832907 0.125297098 0.00000000 14.99278807 #> 454 -26.404944 -24.17396 -24.173964 2.230980087 0.00000000 10.65173122 #> 455 -52.539123 -39.97742 -39.977419 12.561704666 0.00000000 -5.15172326 #> 456 -12.726405 -14.70937 -12.726405 -1.982960923 1.98296092 22.09928988 #> 457 -41.966986 -36.12576 -36.125760 5.841225917 0.00000000 -1.30006435 #> 458 -49.221621 -43.93022 -43.930220 5.291400380 0.00000000 -9.10452504 #> 459 -51.010860 -41.85978 -41.859779 9.151080634 0.00000000 -7.03408355 #> 460 -18.244587 -25.82940 -18.244587 -7.584817069 7.58481707 16.58110850 #> 461 -34.542478 -31.56323 -31.563234 2.979243557 0.00000000 3.26246087 #> 462 -21.038178 -30.14352 -21.038178 -9.105341782 9.10534178 13.78751778 #> 463 -30.912730 -31.20278 -30.912730 -0.290050790 0.29005079 3.91296558 #> 464 -31.983339 -23.85948 -23.859477 8.123862392 0.00000000 10.96621865 #> 465 -35.953317 -37.97291 -35.953317 -2.019590528 2.01959053 -1.12762189 #> 466 -48.813443 -41.14937 -41.149374 7.664068800 0.00000000 -6.32367901 #> 467 -44.051664 -38.05154 -38.051544 6.000120553 0.00000000 -3.22584827 #> 468 -28.167921 -29.27722 -28.167921 -1.109302186 1.10930219 6.65777471 #> 469 -19.199365 -22.68428 -19.199365 -3.484912937 3.48491294 15.62633040 #> 470 -24.635656 -27.00883 -24.635656 -2.373173192 2.37317319 10.19003896 #> 471 -38.373566 -46.28222 -38.373566 -7.908654651 7.90865465 -3.54787049 #> 472 -53.326629 -36.00996 -36.009959 17.316669848 0.00000000 -1.18426370 #> 473 -23.741179 -31.99491 -23.741179 -8.253726252 8.25372625 11.08451643 #> 474 -36.829587 -38.02244 -36.829587 -1.192854218 1.19285422 -2.00389135 #> 475 -27.079921 -30.00773 -27.079921 -2.927805002 2.92780500 7.74577423 #> 476 -58.604785 -50.27755 -50.277550 8.327235040 0.00000000 -15.45185480 #> 477 -33.875914 -35.24758 -33.875914 -1.371664054 1.37166405 0.94978140 #> 478 -48.180093 -45.77968 -45.779680 2.400413477 0.00000000 -10.95398433 #> 479 -58.055256 -57.79063 -57.790631 0.264624996 0.00000000 -22.96493602 #> 480 -20.745155 -22.03950 -20.745155 -1.294341905 1.29434190 14.08054021 #> 481 -29.394351 -33.04709 -29.394351 -3.652734751 3.65273475 5.43134450 #> 482 -50.458030 -39.35587 -39.355874 11.102156756 0.00000000 -4.53017832 #> 483 -29.186569 -29.40917 -29.186569 -0.222597892 0.22259789 5.63912585 #> 484 -26.636414 -35.83730 -26.636414 -9.200887691 9.20088769 8.18928094 #> 485 -49.251887 -36.05800 -36.058001 13.193885882 0.00000000 -1.23230553 #> 486 -30.373973 -32.66693 -30.373973 -2.292957958 2.29295796 4.45172280 #> 487 -19.365675 -27.50661 -19.365675 -8.140930215 8.14093022 15.46002007 #> 488 -79.678864 -65.98274 -65.982739 13.696125048 0.00000000 -31.15704342 #> 489 -27.824948 -32.00825 -27.824948 -4.183297937 4.18329794 7.00074737 #> 490 -67.928752 -49.55983 -49.559834 18.368918604 0.00000000 -14.73413846 #> 491 -34.965478 -30.34624 -30.346244 4.619233456 0.00000000 4.47945084 #> 492 -59.071931 -31.72459 -31.724587 27.347344885 0.00000000 3.10110882 #> 493 -38.043872 -39.89268 -38.043872 -1.848808145 1.84880814 -3.21817653 #> 494 -41.815217 -51.60690 -41.815217 -9.791682772 9.79168277 -6.98952132 #> 495 -120.680862 -78.58210 -78.582101 42.098761734 0.00000000 -43.75640532 #> 496 -24.284295 -26.02300 -24.284295 -1.738704608 1.73870461 10.54140080 #> 497 -24.482993 -31.73269 -24.482993 -7.249693601 7.24969360 10.34270189 #> 498 -56.936619 -47.09342 -47.093419 9.843200012 0.00000000 -12.26772320 #> 499 -38.781073 -28.19426 -28.194261 10.586812386 0.00000000 6.63143442 #> 500 -43.044038 -48.45335 -43.044038 -5.409310461 5.40931046 -8.21834295 #> 501 -47.118025 -38.70137 -38.701368 8.416656399 0.00000000 -3.87567282 #> 502 -30.433807 -26.01824 -26.018236 4.415570891 0.00000000 8.80745959 #> 503 -39.637049 -36.39772 -36.397716 3.239332499 0.00000000 -1.57202109 #> 504 -23.398597 -24.03067 -23.398597 -0.632077148 0.63207715 11.42709806 #> 505 -31.950985 -36.18565 -31.950985 -4.234668662 4.23466866 2.87471040 #> 506 -22.899612 -25.75646 -22.899612 -2.856843470 2.85684347 11.92608294 #> 507 -23.184337 -27.45873 -23.184337 -4.274390473 4.27439047 11.64135790 #> 508 -41.264892 -27.34487 -27.344871 13.920021132 0.00000000 7.48082417 #> 509 -45.728247 -44.07674 -44.076736 1.651511089 0.00000000 -9.25104052 #> 510 -35.348664 -38.89359 -35.348664 -3.544929621 3.54492962 -0.52296864 #> 511 -53.422793 -33.20665 -33.206652 20.216140951 0.00000000 1.61904334 #> 512 -30.229810 -33.53853 -30.229810 -3.308720551 3.30872055 4.59588559 #> 513 -17.233300 -29.85435 -17.233300 -12.621052348 12.62105235 17.59239533 #> 514 -48.343930 -56.03856 -48.343930 -7.694627015 7.69462701 -13.51823466 #> 515 -11.692667 -26.45538 -11.692667 -14.762714168 14.76271417 23.13302788 #> 516 -35.266323 -30.39890 -30.398896 4.867427193 0.00000000 4.42679976 #> 517 -24.193989 -25.91659 -24.193989 -1.722598965 1.72259896 10.63170664 #> 518 -30.529550 -24.99442 -24.994425 5.535125631 0.00000000 9.83127047 #> 519 -32.097426 -28.27028 -28.270282 3.827143794 0.00000000 6.55541339 #> 520 -23.895784 -27.53167 -23.895784 -3.635890978 3.63589098 10.92991137 #> 521 -50.181270 -47.26890 -47.268895 2.912374830 0.00000000 -12.44319983 #> 522 -28.210152 -28.65683 -28.210152 -0.446682444 0.44668244 6.61554371 #> 523 -25.025716 -31.46432 -25.025716 -6.438601917 6.43860192 9.79997960 #> 524 -17.317966 -19.47380 -17.317966 -2.155830210 2.15583021 17.50772956 #> 525 -40.595936 -35.89790 -35.897898 4.698038545 0.00000000 -1.07220253 #> 526 -32.815013 -41.01822 -32.815013 -8.203203816 8.20320382 2.01068211 #> 527 -25.756220 -25.44908 -25.449085 0.307135174 0.00000000 9.37661044 #> 528 -32.843404 -35.77822 -32.843404 -2.934817445 2.93481744 1.98229142 #> 529 -29.495385 -28.35029 -28.350286 1.145098050 0.00000000 6.47540887 #> 530 -34.054072 -36.33171 -34.054072 -2.277641152 2.27764115 0.77162346 #> 531 -14.904893 -23.73310 -14.904893 -8.828210720 8.82821072 19.92080213 #> 532 -37.696372 -36.20627 -36.206269 1.490103016 0.00000000 -1.38057354 #> 533 -20.261330 -17.97789 -17.977894 2.283435258 0.00000000 16.84780103 #> 534 -36.123215 -34.71312 -34.713124 1.410091032 0.00000000 0.11257152 #> 535 -34.169734 -27.43623 -27.436230 6.733503642 0.00000000 7.38946542 #> 536 -29.920431 -24.72467 -24.724668 5.195762399 0.00000000 10.10102711 #> 537 -18.960910 -26.91727 -18.960910 -7.956363094 7.95636309 15.86478577 #> 538 -31.468259 -35.28228 -31.468259 -3.814020774 3.81402077 3.35743675 #> 539 -40.019154 -40.08706 -40.019154 -0.067909089 0.06790909 -5.19345893 #> 540 -20.420177 -20.73350 -20.420177 -0.313321811 0.31332181 14.40551872 #> 541 -38.425118 -33.81820 -33.818195 4.606923159 0.00000000 1.00750023 #> 542 -22.726644 -34.96936 -22.726644 -12.242711856 12.24271186 12.09905167 #> 543 -43.650991 -57.94800 -43.650991 -14.297004944 14.29700494 -8.82529531 #> 544 -33.524695 -25.90479 -25.904795 7.619900716 0.00000000 8.92090057 #> 545 -29.273676 -32.55336 -29.273676 -3.279682206 3.27968221 5.55201952 #> 546 -29.749748 -29.43461 -29.434609 0.315139614 0.00000000 5.39108654 #> 547 -42.449118 -35.57179 -35.571793 6.877324604 0.00000000 -0.74609796 #> 548 -29.374824 -33.96608 -29.374824 -4.591257048 4.59125705 5.45087179 #> 549 -62.199825 -49.38354 -49.383541 12.816283206 0.00000000 -14.55784612 #> 550 -25.731060 -29.90379 -25.731060 -4.172726886 4.17272689 9.09463502 #> 551 -32.200386 -29.07722 -29.077218 3.123167161 0.00000000 5.74847694 #> 552 -29.533295 -34.01888 -29.533295 -4.485583952 4.48558395 5.29240057 #> 553 -20.618117 -29.72542 -20.618117 -9.107298607 9.10729861 14.20757784 #> 554 -31.613999 -44.34537 -31.613999 -12.731371587 12.73137159 3.21169634 #> 555 -34.124607 -21.34105 -21.341046 12.783560443 0.00000000 13.48464887 #> 556 -41.083825 -37.24803 -37.248029 3.835795764 0.00000000 -2.42233394 #> 557 -33.369162 -27.33603 -27.336030 6.033132212 0.00000000 7.48966567 #> 558 -31.057345 -35.79678 -31.057345 -4.739433177 4.73943318 3.76834989 #> 559 -29.123738 -27.66119 -27.661192 1.462545826 0.00000000 7.16450347 #> 560 -14.947314 -22.38510 -14.947314 -7.437786460 7.43778646 19.87838174 #> 561 -33.283995 -31.43934 -31.439336 1.844659717 0.00000000 3.38635974 #> 562 -51.845794 -37.46412 -37.464121 14.381673305 0.00000000 -2.63842524 #> 563 -32.516771 -37.84996 -32.516771 -5.333192666 5.33319267 2.30892417 #> 564 -53.277806 -45.74146 -45.741455 7.536350624 0.00000000 -10.91575973 #> 565 -28.557342 -40.49259 -28.557342 -11.935250065 11.93525007 6.26835347 #> 566 -57.916324 -42.93395 -42.933955 14.982369209 0.00000000 -8.10825921 #> 567 -57.255882 -36.22085 -36.220855 21.035027268 0.00000000 -1.39515961 #> 568 -19.822973 -21.86792 -19.822973 -2.044944378 2.04494438 15.00272185 #> 569 -30.084329 -32.28934 -30.084329 -2.205015788 2.20501579 4.74136622 #> 570 -54.050152 -50.64909 -50.649087 3.401064691 0.00000000 -15.82339212 #> 571 -19.141970 -25.18434 -19.141970 -6.042370802 6.04237080 15.68372496 #> 572 -48.721133 -43.34580 -43.345804 5.375329569 0.00000000 -8.52010834 #> 573 -30.780764 -36.05100 -30.780764 -5.270234925 5.27023492 4.04493168 #> 574 -42.170589 -40.87077 -40.870765 1.299823515 0.00000000 -6.04506989 #> 575 -21.545939 -23.69349 -21.545939 -2.147546556 2.14754656 13.27975599 #> 576 -74.661176 -71.36924 -71.369245 3.291931286 0.00000000 -36.54354945 #> 577 -49.700792 -41.70621 -41.706210 7.994581502 0.00000000 -6.88051507 #> 578 -19.651713 -24.61340 -19.651713 -4.961687066 4.96168707 15.17398225 #> 579 -34.061458 -35.02832 -34.061458 -0.966860914 0.96686091 0.76423734 #> 580 -83.974274 -56.84799 -56.847989 27.126285008 0.00000000 -22.02229321 #> 581 -30.483444 -30.63742 -30.483444 -0.153977186 0.15397719 4.34225104 #> 582 -30.258658 -33.03482 -30.258658 -2.776160827 2.77616083 4.56703775 #> 583 -24.760829 -29.27274 -24.760829 -4.511908177 4.51190818 10.06486656 #> 584 -42.664267 -46.78795 -42.664267 -4.123681582 4.12368158 -7.83857145 #> 585 -28.346076 -34.07851 -28.346076 -5.732436302 5.73243630 6.47961965 #> 586 -32.765553 -22.52607 -22.526072 10.239480544 0.00000000 12.29962300 #> 587 -56.913061 -38.97007 -38.970065 17.942995789 0.00000000 -4.14436992 #> 588 -33.910981 -32.74086 -32.740857 1.170123335 0.00000000 2.08483804 #> 589 -44.907666 -41.21730 -41.217298 3.690367321 0.00000000 -6.39160312 #> 590 -49.050170 -50.49458 -49.050170 -1.444408955 1.44440895 -14.22447420 #> 591 -31.419126 -26.27038 -26.270379 5.148746933 0.00000000 8.55531590 #> 592 -16.566955 -20.09725 -16.566955 -3.530299423 3.53029942 18.25874010 #> 593 -20.743652 -27.11818 -20.743652 -6.374525598 6.37452560 14.08204372 #> 594 -35.965556 -32.75033 -32.750328 3.215228349 0.00000000 2.07536722 #> 595 -39.012811 -50.97758 -39.012811 -11.964772560 11.96477256 -4.18711558 #> 596 -31.774414 -33.06533 -31.774414 -1.290920507 1.29092051 3.05128158 #> 597 -36.623598 -26.41388 -26.413884 10.209713865 0.00000000 8.41181097 #> 598 -37.623354 -40.49778 -37.623354 -2.874420878 2.87442088 -2.79765911 #> 599 -40.803563 -32.01117 -32.011170 8.792393039 0.00000000 2.81452515 #> 600 -18.538313 -23.53675 -18.538313 -4.998437657 4.99843766 16.28738195 #> 601 -27.217819 -34.24666 -27.217819 -7.028842100 7.02884210 7.60787593 #> 602 -32.396334 -32.02075 -32.020750 0.375583738 0.00000000 2.80494544 #> 603 -28.812184 -31.26342 -28.812184 -2.451237807 2.45123781 6.01351161 #> 604 -21.641477 -25.46970 -21.641477 -3.828222002 3.82822200 13.18421832 #> 605 -24.335938 -22.86293 -22.862934 1.473004000 0.00000000 11.96276107 #> 606 -28.565620 -37.81985 -28.565620 -9.254229998 9.25423000 6.26007501 #> 607 -34.686362 -33.62957 -33.629570 1.056792247 0.00000000 1.19612514 #> 608 -37.593407 -29.24701 -29.247010 8.346396833 0.00000000 5.57868556 #> 609 -21.723745 -22.83200 -21.723745 -1.108257352 1.10825735 13.10195001 #> 610 -30.844217 -43.38884 -30.844217 -12.544621174 12.54462117 3.98147850 #> 611 -31.453692 -32.24849 -31.453692 -0.794799048 0.79479905 3.37200357 #> 612 -18.280651 -18.95907 -18.280651 -0.678418631 0.67841863 16.54504473 #> 613 -27.559451 -27.72912 -27.559451 -0.169667908 0.16966791 7.26624431 #> 614 -38.878418 -39.11318 -38.878418 -0.234761683 0.23476168 -4.05272316 #> 615 -45.026930 -35.36524 -35.365235 9.661694063 0.00000000 -0.53954011 #> 616 -31.674043 -29.74781 -29.747808 1.926234730 0.00000000 5.07788686 #> 617 -21.262957 -18.57031 -18.570306 2.692650886 0.00000000 16.25538922 #> 618 -16.240462 -24.70101 -16.240462 -8.460549020 8.46054902 18.58523317 #> 619 -36.988440 -38.17125 -36.988440 -1.182808117 1.18280812 -2.16274473 #> 620 -29.996054 -39.98557 -29.996054 -9.989520149 9.98952015 4.82964117 #> 621 -38.256518 -38.68127 -38.256518 -0.424750509 0.42475051 -3.43082266 #> 622 -45.171302 -44.43136 -44.431358 0.739944094 0.00000000 -9.60566262 #> 623 -17.295390 -19.43233 -17.295390 -2.136938190 2.13693819 17.53030539 #> 624 -22.786569 -33.13922 -22.786569 -10.352649238 10.35264924 12.03912585 #> 625 -26.297341 -22.37879 -22.378791 3.918549440 0.00000000 12.44690419 #> 626 -33.884220 -34.71640 -33.884220 -0.832182948 0.83218295 0.94147490 #> 627 -36.179015 -33.64639 -33.646388 2.532627027 0.00000000 1.17930747 #> 628 -55.569875 -48.86676 -48.866757 6.703117495 0.00000000 -14.04106213 #> 629 -23.678868 -24.23941 -23.678868 -0.560546383 0.56054638 11.14682708 #> 630 -103.000028 -82.64507 -82.645070 20.354957993 0.00000000 -47.81937423 #> 631 -27.673873 -30.00550 -27.673873 -2.331622525 2.33162253 7.15182198 #> 632 -38.244451 -36.31110 -36.311098 1.933352554 0.00000000 -1.48540305 #> 633 -24.206030 -33.00672 -24.206030 -8.800689391 8.80068939 10.61966537 #> 634 -26.502719 -27.36077 -26.502719 -0.858052667 0.85805267 8.32297645 #> 635 -27.612222 -24.78012 -24.780122 2.832100092 0.00000000 10.04557372 #> 636 -28.307704 -28.40625 -28.307704 -0.098543786 0.09854379 6.51799175 #> 637 -24.300246 -25.88779 -24.300246 -1.587540959 1.58754096 10.52544960 #> 638 -27.650521 -27.17288 -27.172876 0.477644926 0.00000000 7.65281945 #> 639 -23.034904 -32.84395 -23.034904 -9.809051081 9.80905108 11.79079171 #> 640 -31.007796 -36.02797 -31.007796 -5.020172748 5.02017275 3.81789981 #> 641 -36.923811 -32.64128 -32.641276 4.282535227 0.00000000 2.18441909 #> 642 -28.739272 -33.46871 -28.739272 -4.729435601 4.72943560 6.08642346 #> 643 -27.207530 -24.67129 -24.671295 2.536234927 0.00000000 10.15440053 #> 644 -46.155008 -48.42937 -46.155008 -2.274366155 2.27436616 -11.32931262 #> 645 -32.877177 -34.59436 -32.877177 -1.717187169 1.71718717 1.94851849 #> 646 -21.039416 -30.64949 -21.039416 -9.610071218 9.61007122 13.78627893 #> 647 -12.535232 -17.02552 -12.535232 -4.490291396 4.49029140 22.29046372 #> 648 -71.752316 -55.70931 -55.709307 16.043009436 0.00000000 -20.88361148 #> 649 -57.564649 -50.01446 -50.014464 7.550185449 0.00000000 -15.18876870 #> 650 -14.534147 -19.02088 -14.534147 -4.486732998 4.48673300 20.29154801 #> 651 -43.861010 -30.92012 -30.920120 12.940889257 0.00000000 3.90557490 #> 652 -100.581078 -68.63295 -68.632955 31.948122885 0.00000000 -33.80725961 #> 653 -46.932119 -32.47424 -32.474241 14.457878080 0.00000000 2.35145399 #> 654 -23.483257 -32.60558 -23.483257 -9.122321408 9.12232141 11.34243827 #> 655 -41.619349 -35.22786 -35.227860 6.391488589 0.00000000 -0.40216482 #> 656 -37.565839 -26.93428 -26.934279 10.631560084 0.00000000 7.89141657 #> 657 -36.978212 -26.44392 -26.443918 10.534294263 0.00000000 8.38177729 #> 658 -18.762839 -19.07872 -18.762839 -0.315882319 0.31588232 16.06285617 #> 659 -23.881180 -30.69811 -23.881180 -6.816931202 6.81693120 10.94451519 #> 660 -20.212419 -24.23479 -20.212419 -4.022370194 4.02237019 14.61327653 #> 661 -27.386252 -29.60026 -27.386252 -2.214011508 2.21401151 7.43944358 #> 662 -33.760830 -29.95261 -29.952607 3.808222942 0.00000000 4.87308810 #> 663 -25.424113 -25.84590 -25.424113 -0.421787206 0.42178721 9.40158240 #> 664 -25.917525 -20.65974 -20.659738 5.257786994 0.00000000 14.16595695 #> 665 -37.985305 -35.18610 -35.186096 2.799209004 0.00000000 -0.36040095 #> 666 -54.452142 -41.55452 -41.554517 12.897625241 0.00000000 -6.72882132 #> 667 -40.430195 -35.31724 -35.317237 5.112957935 0.00000000 -0.49154164 #> 668 -43.338399 -40.45948 -40.459481 2.878917871 0.00000000 -5.63378563 #> 669 -25.503570 -36.38378 -25.503570 -10.880205803 10.88020580 9.32212577 #> 670 -43.904426 -36.63928 -36.639281 7.265144596 0.00000000 -1.81358591 #> 671 -57.185793 -53.11213 -53.112133 4.073660377 0.00000000 -18.28643749 #> 672 -47.848734 -42.76211 -42.762109 5.086625315 0.00000000 -7.93641350 #> 673 -34.882318 -34.38561 -34.385609 0.496709613 0.00000000 0.44008646 #> 674 -29.147228 -29.66610 -29.147228 -0.518872150 0.51887215 5.67846778 #> 675 -48.482399 -38.37061 -38.370610 10.111788557 0.00000000 -3.54491498 #> 676 -16.981128 -20.64239 -16.981128 -3.661265346 3.66126535 17.84456724 #> 677 -36.127876 -26.46127 -26.461274 9.666602109 0.00000000 8.36442115 #> 678 -16.293340 -22.00544 -16.293340 -5.712095424 5.71209542 18.53235503 #> 679 -20.779916 -21.38226 -20.779916 -0.602342595 0.60234259 14.04577922 #> 680 -27.156718 -33.60660 -27.156718 -6.449882809 6.44988281 7.66897712 #> 681 -42.291883 -35.52098 -35.520982 6.770901353 0.00000000 -0.69528644 #> 682 -29.271517 -32.05807 -29.271517 -2.786554577 2.78655458 5.55417813 #> 683 -27.628463 -26.40190 -26.401900 1.226562461 0.00000000 8.42379511 #> 684 -22.226143 -23.06578 -22.226143 -0.839638579 0.83963858 12.59955211 #> 685 -54.185047 -35.93904 -35.939040 18.246007318 0.00000000 -1.11334448 #> 686 -28.152602 -26.69086 -26.690856 1.461745440 0.00000000 8.13483902 #> 687 -27.376054 -26.38231 -26.382306 0.993748465 0.00000000 8.44338982 #> 688 -24.931914 -26.86728 -24.931914 -1.935362703 1.93536270 9.89378157 #> 689 -54.719323 -46.91228 -46.912283 7.807040141 0.00000000 -12.08658745 #> 690 -19.800244 -25.10816 -19.800244 -5.307920373 5.30792037 15.02545097 #> 691 -14.804353 -17.44094 -14.804353 -2.636588352 2.63658835 20.02134198 #> 692 -29.794403 -33.23988 -29.794403 -3.445475370 3.44547537 5.03129277 #> 693 -35.599124 -35.62242 -35.599124 -0.023292316 0.02329232 -0.77342897 #> 694 -24.134607 -29.07750 -24.134607 -4.942895030 4.94289503 10.69108869 #> 695 -34.809486 -53.75713 -34.809486 -18.947641436 18.94764144 0.01620974 #> 696 -41.974096 -29.98304 -29.983039 11.991056841 0.00000000 4.84265633 #> 697 -53.755659 -42.30166 -42.301660 11.453999651 0.00000000 -7.47596448 #> 698 -29.691431 -35.15264 -29.691431 -5.461207457 5.46120746 5.13426402 #> 699 -48.493545 -36.62196 -36.621965 11.871580555 0.00000000 -1.79626930 #> 700 -17.584084 -20.56742 -17.584084 -2.983336704 2.98333670 17.24161154 #> 701 -17.518956 -23.63513 -17.518956 -6.116176247 6.11617625 17.30673940 #> 702 -26.261052 -28.89561 -26.261052 -2.634560389 2.63456039 8.56464313 #> 703 -41.187338 -48.70107 -41.187338 -7.513732812 7.51373281 -6.36164238 #> 704 -48.483854 -55.23514 -48.483854 -6.751284858 6.75128486 -13.65815887 #> 705 -32.240835 -26.00339 -26.003389 6.237445276 0.00000000 8.82230608 #> 706 -44.028389 -39.78822 -39.788225 4.240164306 0.00000000 -4.96252942 #> 707 -69.791359 -67.62816 -67.628157 2.163202160 0.00000000 -32.80246194 #> 708 -45.958216 -36.00022 -36.000224 9.957992246 0.00000000 -1.17452847 #> 709 -16.686028 -24.08880 -16.686028 -7.402771656 7.40277166 18.13966776 #> 710 -34.524775 -32.03828 -32.038283 2.486491689 0.00000000 2.78741203 #> 711 -33.074563 -31.98969 -31.989693 1.084869648 0.00000000 2.83600198 #> 712 -24.072121 -33.79518 -24.072121 -9.723059976 9.72305998 10.75357461 #> 713 -50.905861 -45.65743 -45.657432 5.248429227 0.00000000 -10.83173693 #> 714 -22.458418 -22.80009 -22.458418 -0.341667464 0.34166746 12.36727777 #> 715 -29.877399 -32.09471 -29.877399 -2.217311640 2.21731164 4.94829652 #> 716 -57.616640 -43.88616 -43.886160 13.730480109 0.00000000 -9.06046462 #> 717 -70.737329 -64.36822 -64.368219 6.369110676 0.00000000 -29.54252343 #> 718 -35.589352 -33.37425 -33.374247 2.215104127 0.00000000 1.45144790 #> 719 -44.101915 -33.58816 -33.588163 10.513752693 0.00000000 1.23753266 #> 720 -47.065828 -45.67680 -45.676804 1.389023826 0.00000000 -10.85110859 #> 721 -17.378111 -20.60717 -17.378111 -3.229064179 3.22906418 17.44758462 #> 722 -48.154047 -47.62427 -47.624274 0.529772627 0.00000000 -12.79857893 #> 723 -20.051748 -25.59710 -20.051748 -5.545352971 5.54535297 14.77394716 #> 724 -24.296848 -34.31322 -24.296848 -10.016367825 10.01636782 10.52884740 #> 725 -19.983276 -30.92242 -19.983276 -10.939148988 10.93914899 14.84241965 #> 726 -36.220091 -34.70039 -34.700393 1.519697989 0.00000000 0.12530189 #> 727 -35.139692 -38.20258 -35.139692 -3.062891970 3.06289197 -0.31399643 #> 728 -36.661787 -38.14670 -36.661787 -1.484916533 1.48491653 -1.83609190 #> 729 -35.460999 -41.62391 -35.460999 -6.162907593 6.16290759 -0.63530353 #> 730 -36.384223 -29.42664 -29.426638 6.957585217 0.00000000 5.39905749 #> 731 -55.255368 -66.24716 -55.255368 -10.991787221 10.99178722 -20.42967256 #> 732 -45.784339 -46.34354 -45.784339 -0.559205458 0.55920546 -10.95864395 #> 733 -55.467076 -41.48269 -41.482694 13.984382145 0.00000000 -6.65699843 #> 734 -37.840622 -26.24507 -26.245075 11.595547610 0.00000000 8.58062056 #> 735 -64.659836 -49.28398 -49.283979 15.375856400 0.00000000 -14.45828397 #> 736 -39.613551 -37.61779 -37.617789 1.995762069 0.00000000 -2.79209393 #> 737 -42.686475 -51.68149 -42.686475 -8.995017727 8.99501773 -7.86077972 #> 738 -41.977859 -41.84302 -41.843021 0.134838210 0.00000000 -7.01732583 #> 739 -39.777808 -41.03040 -39.777808 -1.252589406 1.25258941 -4.95211292 #> 740 -40.140699 -35.14091 -35.140910 4.999789465 0.00000000 -0.31521423 #> 741 -34.482068 -35.60986 -34.482068 -1.127794217 1.12779422 0.34362758 #> 742 -21.319758 -30.23062 -21.319758 -8.910857166 8.91085717 13.50593749 #> 743 -27.065400 -45.94810 -27.065400 -18.882704101 18.88270410 7.76029535 #> 744 -53.464070 -50.19521 -50.195211 3.268858234 0.00000000 -15.36951612 #> 745 -29.694765 -52.32428 -29.694765 -22.629518392 22.62951839 5.13092994 #> 746 -22.246166 -21.68257 -21.682570 0.563596276 0.00000000 13.14312517 #> 747 -30.606060 -43.34910 -30.606060 -12.743036247 12.74303625 4.21963563 #> 748 -31.643657 -52.38442 -31.643657 -20.740767803 20.74076780 3.18203855 #> 749 -29.768552 -29.36097 -29.360967 0.407584692 0.00000000 5.46472803 #> 750 -27.278161 -30.56588 -27.278161 -3.287718486 3.28771849 7.54753431 #> 751 -41.608154 -43.13469 -41.608154 -1.526532433 1.52653243 -6.78245896 #> 752 -23.877312 -28.61065 -23.877312 -4.733342413 4.73334241 10.94838341 #> 753 -38.314867 -37.97133 -37.971325 0.343541754 0.00000000 -3.14563015 #> 754 -22.302942 -25.37243 -22.302942 -3.069484780 3.06948478 12.52275283 #> 755 -21.061007 -25.69744 -21.061007 -4.636435243 4.63643524 13.76468853 #> 756 -60.362083 -43.48413 -43.484126 16.877956762 0.00000000 -8.65843107 #> 757 -19.005495 -32.59850 -19.005495 -13.593000732 13.59300073 15.82020060 #> 758 -45.601031 -50.15290 -45.601031 -4.551865603 4.55186560 -10.77533614 #> 759 -38.358107 -36.12408 -36.124075 2.234031789 0.00000000 -1.29837991 #> 760 -24.441087 -28.49494 -24.441087 -4.053857230 4.05385723 10.38460819 #> 761 -36.354903 -52.25131 -36.354903 -15.896404190 15.89640419 -1.52920786 #> 762 -69.971236 -50.91848 -50.918483 19.052753389 0.00000000 -16.09278718 #> 763 -38.165425 -22.57157 -22.571574 15.593850287 0.00000000 12.25412099 #> 764 -22.920200 -21.48503 -21.485030 1.435169768 0.00000000 13.34066491 #> 765 -44.052940 -41.28027 -41.280275 2.772665256 0.00000000 -6.45457922 #> 766 -36.630057 -34.30833 -34.308333 2.321723398 0.00000000 0.51736221 #> 767 -46.067538 -37.22689 -37.226892 8.840645753 0.00000000 -2.40119710 #> 768 -75.262071 -60.16425 -60.164250 15.097820462 0.00000000 -25.33855494 #> 769 -19.301502 -26.31245 -19.301502 -7.010950100 7.01095010 15.52419363 #> 770 -37.798463 -35.18268 -35.182678 2.615785316 0.00000000 -0.35698266 #> 771 -56.605978 -43.78634 -43.786337 12.819641510 0.00000000 -8.96064125 #> 772 -30.158137 -29.60119 -29.601194 0.556942745 0.00000000 5.22450147 #> 773 -70.581256 -57.22425 -57.224248 13.357008360 0.00000000 -22.39855225 #> 774 -83.328383 -63.49388 -63.493885 19.834498709 0.00000000 -28.66818923 #> 775 -27.383194 -24.83538 -24.835380 2.547813896 0.00000000 9.99031555 #> 776 -24.687533 -26.68595 -24.687533 -1.998413049 1.99841305 10.13816211 #> 777 -38.168919 -38.65385 -38.168919 -0.484930753 0.48493075 -3.34322371 #> 778 -23.361377 -24.63014 -23.361377 -1.268765722 1.26876572 11.46431800 #> 779 -36.144074 -40.11087 -36.144074 -3.966796929 3.96679693 -1.31837865 #> 780 -83.367959 -52.68565 -52.685647 30.682311674 0.00000000 -17.85995157 #> 781 -17.280820 -27.71350 -17.280820 -10.432678822 10.43267882 17.54487483 #> 782 -19.374350 -27.84479 -19.374350 -8.470438349 8.47043835 15.45134506 #> 783 -26.036520 -26.12037 -26.036520 -0.083853378 0.08385338 8.78917537 #> 784 -32.273956 -35.94733 -32.273956 -3.673369539 3.67336954 2.55173917 #> 785 -36.317834 -40.42136 -36.317834 -4.103523354 4.10352335 -1.49213892 #> 786 -66.972223 -63.85969 -63.859693 3.112530368 0.00000000 -29.03399750 #> 787 -33.496078 -33.55759 -33.496078 -0.061508774 0.06150877 1.32961689 #> 788 -14.726783 -23.49375 -14.726783 -8.766962093 8.76696209 20.09891197 #> 789 -18.314830 -27.87102 -18.314830 -9.556194688 9.55619469 16.51086504 #> 790 -11.859303 -24.05484 -11.859303 -12.195538672 12.19553867 22.96639242 #> 791 -38.302503 -38.12831 -38.128314 0.174188914 0.00000000 -3.30261837 #> 792 -16.518990 -23.74759 -16.518990 -7.228596022 7.22859602 18.30670565 #> 793 -49.990470 -36.03803 -36.038029 13.952440859 0.00000000 -1.21233369 #> 794 -35.058645 -42.18463 -35.058645 -7.125987807 7.12598781 -0.23294988 #> 795 -24.735892 -26.26556 -24.735892 -1.529672750 1.52967275 10.08980315 #> 796 -21.528604 -22.66836 -21.528604 -1.139751978 1.13975198 13.29709159 #> 797 -50.285771 -33.74901 -33.749010 16.536761000 0.00000000 1.07668502 #> 798 -27.240957 -30.82440 -27.240957 -3.583444467 3.58344447 7.58473852 #> 799 -35.039390 -39.07693 -35.039390 -4.037536872 4.03753687 -0.21369448 #> 800 -26.815881 -26.17295 -26.172952 0.642929622 0.00000000 8.65274350 #> 801 -30.018330 -38.08396 -30.018330 -8.065627475 8.06562747 4.80736495 #> 802 -32.817912 -31.90774 -31.907744 0.910167574 0.00000000 2.91795118 #> 803 -21.702739 -30.03743 -21.702739 -8.334694815 8.33469482 13.12295669 #> 804 -28.186748 -31.66415 -28.186748 -3.477398952 3.47739895 6.63894696 #> 805 -51.255412 -38.88664 -38.886635 12.368777151 0.00000000 -4.06093977 #> 806 -37.227154 -38.04851 -37.227154 -0.821352542 0.82135254 -2.40145868 #> 807 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18.564924096 0.00000000 -8.72454248 #> 821 -12.021851 -18.15487 -12.021851 -6.133018843 6.13301884 22.80384441 #> 822 -32.292306 -42.71255 -32.292306 -10.420244916 10.42024492 2.53338895 #> 823 -51.445604 -54.64531 -51.445604 -3.199708976 3.19970898 -16.61990889 #> 824 -27.138755 -26.94209 -26.942089 0.196666211 0.00000000 7.88360677 #> 825 -42.891233 -29.17545 -29.175451 13.715782184 0.00000000 5.65024460 #> 826 -26.652664 -33.80428 -26.652664 -7.151616652 7.15161665 8.17303116 #> 827 -58.106004 -33.70801 -33.708009 24.397995404 0.00000000 1.11768671 #> 828 -75.913976 -55.40180 -55.401802 20.512173741 0.00000000 -20.57610717 #> 829 -22.164701 -24.85288 -22.164701 -2.688176870 2.68817687 12.66099392 #> 830 -24.296165 -25.47363 -24.296165 -1.177462714 1.17746271 10.52953055 #> 831 -41.505855 -36.35584 -36.355844 5.150011296 0.00000000 -1.53014886 #> 832 -25.184077 -25.17551 -25.175506 0.008571006 0.00000000 9.65018921 #> 833 -34.960619 -31.77049 -31.770488 3.190130825 0.00000000 3.05520706 #> 834 -28.337895 -42.63458 -28.337895 -14.296685386 14.29668539 6.48780042 #> 835 -37.132266 -50.89076 -37.132266 -13.758492362 13.75849236 -2.30657094 #> 836 -46.812858 -39.05814 -39.058139 7.754718957 0.00000000 -4.23244363 #> 837 -25.360168 -21.67096 -21.670965 3.689203044 0.00000000 13.15473038 #> 838 -80.307314 -72.27332 -72.273320 8.033993579 0.00000000 -37.44762474 #> 839 -23.865747 -21.64710 -21.647097 2.218650298 0.00000000 13.17859863 #> 840 -28.692002 -24.52981 -24.529809 4.162193259 0.00000000 10.29588663 #> 841 -38.864104 -35.69677 -35.696774 3.167330129 0.00000000 -0.87107827 #> 842 -28.830840 -27.45771 -27.457715 1.373125695 0.00000000 7.36798069 #> 843 -52.338228 -38.43021 -38.430210 13.908017925 0.00000000 -3.60451506 #> 844 -38.194302 -42.21644 -38.194302 -4.022135571 4.02213557 -3.36860621 #> 845 -51.267761 -56.58512 -51.267761 -5.317357640 5.31735764 -16.44206553 #> 846 -27.425589 -26.91016 -26.910158 0.515430892 0.00000000 7.91553716 #> 847 -46.964820 -34.14595 -34.145950 12.818869799 0.00000000 0.67974487 #> 848 -69.948361 -58.21453 -58.214535 11.733826502 0.00000000 -23.38883926 #> 849 -22.565197 -31.35577 -22.565197 -8.790577480 8.79057748 12.26049838 #> 850 -52.752755 -46.71495 -46.714950 6.037805442 0.00000000 -11.88925455 #> 851 -46.358195 -41.23692 -41.236922 5.121273542 0.00000000 -6.41122656 #> 852 -35.892906 -39.78711 -35.892906 -3.894200965 3.89420096 -1.06721063 #> 853 -40.202667 -40.72536 -40.202667 -0.522689015 0.52268901 -5.37697145 #> 854 -41.625298 -43.61490 -41.625298 -1.989598279 1.98959828 -6.79960307 #> 855 -27.528954 -21.91579 -21.915789 5.613165354 0.00000000 12.90990675 #> 856 -55.571381 -46.35836 -46.358360 9.213021711 0.00000000 -11.53266437 #> 857 -34.402609 -34.30217 -34.302174 0.100434950 0.00000000 0.52352154 #> 858 -22.756093 -24.37069 -22.756093 -1.614595722 1.61459572 12.06960235 #> 859 -38.680828 -38.17853 -38.178528 0.502300410 0.00000000 -3.35283236 #> 860 -49.234869 -41.83267 -41.832666 7.402202945 0.00000000 -7.00697117 #> 861 -38.960149 -41.15109 -38.960149 -2.190941338 2.19094134 -4.13445397 #> 862 -63.857382 -54.10906 -54.109059 9.748323302 0.00000000 -19.28336341 #> 863 -31.177706 -29.93048 -29.930485 1.247220738 0.00000000 4.89521048 #> 864 -40.363029 -35.32875 -35.328749 5.034279633 0.00000000 -0.50305356 #> 865 -32.514886 -34.11995 -32.514886 -1.605066510 1.60506651 2.31080930 #> 866 -46.579931 -39.91749 -39.917493 6.662437860 0.00000000 -5.09179770 #> 867 -25.594053 -25.73878 -25.594053 -0.144731614 0.14473161 9.23164228 #> 868 -26.280421 -24.54068 -24.540677 1.739743976 0.00000000 10.28501862 #> 869 -31.059032 -25.87988 -25.879884 5.179147124 0.00000000 8.94581090 #> 870 -26.925822 -27.31467 -26.925822 -0.388847335 0.38884733 7.89987313 #> 871 -48.302524 -61.13593 -48.302524 -12.833404131 12.83340413 -13.47682876 #> 872 -52.343015 -39.08331 -39.083312 13.259703467 0.00000000 -4.25761632 #> 873 -28.070898 -28.10876 -28.070898 -0.037858308 0.03785831 6.75479710 #> 874 -30.596681 -35.01496 -30.596681 -4.418280693 4.41828069 4.22901480 #> 875 -17.113713 -19.86929 -17.113713 -2.755576082 2.75557608 17.71198194 #> 876 -30.988352 -25.60191 -25.601910 5.386442115 0.00000000 9.22378577 #> 877 -24.302616 -22.38162 -22.381616 1.921000034 0.00000000 12.44407907 #> 878 -36.927773 -22.15007 -22.150067 14.777705876 0.00000000 12.67562812 #> 879 -42.768976 -41.20309 -41.203093 1.565883026 0.00000000 -6.37739780 #> 880 -27.810045 -23.77593 -23.775933 4.034111950 0.00000000 11.04976206 #> 881 -27.035196 -27.53486 -27.035196 -0.499663718 0.49966372 7.79049899 #> 882 -26.633828 -23.72083 -23.720833 2.912995124 0.00000000 11.10486214 #> 883 -11.603238 -19.34979 -11.603238 -7.746551719 7.74655172 23.22245695 #> 884 -37.350648 -25.21385 -25.213846 12.136802115 0.00000000 9.61184955 #> 885 -35.764025 -51.28410 -35.764025 -15.520077634 15.52007763 -0.93832968 #> 886 -30.528418 -29.19541 -29.195413 1.333005028 0.00000000 5.63028225 #> 887 -19.664684 -33.00711 -19.664684 -13.342427453 13.34242745 15.16101105 #> 888 -26.945836 -30.73729 -26.945836 -3.791452506 3.79145251 7.87985913 #> 889 -31.795032 -32.25140 -31.795032 -0.456369018 0.45636902 3.03066355 #> 890 -19.864072 -25.46546 -19.864072 -5.601384196 5.60138420 14.96162379 #> 891 -43.449781 -34.84675 -34.846752 8.603028898 0.00000000 -0.02105685 #> 892 -48.431579 -36.92244 -36.922436 11.509142934 0.00000000 -2.09674061 #> 893 -58.545939 -51.24628 -51.246277 7.299662749 0.00000000 -16.42058125 #> 894 -44.473891 -37.53303 -37.533026 6.940864523 0.00000000 -2.70733077 #> 895 -27.464096 -25.18566 -25.185664 2.278432407 0.00000000 9.64003177 #> 896 -31.985743 -27.28566 -27.285655 4.700088091 0.00000000 7.54004019 #> 897 -28.629047 -28.67101 -28.629047 -0.041960933 0.04196093 6.19664824 #> 898 -24.022227 -22.64191 -22.641909 1.380317867 0.00000000 12.18378611 #> 899 -39.767679 -40.65619 -39.767679 -0.888515191 0.88851519 -4.94198417 #> 900 -27.058792 -29.70067 -27.058792 -2.641882610 2.64188261 7.76690335 #> 901 -50.714242 -35.89239 -35.892392 14.821849899 0.00000000 -1.06669708 #> 902 -41.618441 -27.90616 -27.906156 13.712284769 0.00000000 6.91953917 #> 903 -23.858741 -28.49932 -23.858741 -4.640583769 4.64058377 10.96695416 #> 904 -29.713827 -33.99794 -29.713827 -4.284116260 4.28411626 5.11186853 #> 905 -41.751568 -50.67227 -41.751568 -8.920703846 8.92070385 -6.92587272 #> 906 -41.354902 -35.59825 -35.598254 5.756648325 0.00000000 -0.77255869 #> 907 -22.441692 -24.59212 -22.441692 -2.150427818 2.15042782 12.38400289 #> 908 -23.711423 -30.97018 -23.711423 -7.258754464 7.25875446 11.11427272 #> 909 -22.952597 -27.77520 -22.952597 -4.822602851 4.82260285 11.87309822 #> 910 -34.623674 -29.30186 -29.301858 5.321815711 0.00000000 5.52383747 #> 911 -38.242953 -36.63384 -36.633844 1.609108360 0.00000000 -1.80814882 #> 912 -36.578542 -32.44066 -32.440657 4.137884817 0.00000000 2.38503810 #> 913 -19.409410 -28.23941 -19.409410 -8.830003423 8.83000342 15.41628559 #> 914 -30.291435 -32.47680 -30.291435 -2.185368378 2.18536838 4.53426021 #> 915 -41.488242 -34.55997 -34.559969 6.928273201 0.00000000 0.26572671 #> 916 -27.670235 -28.94721 -27.670235 -1.276976270 1.27697627 7.15545988 #> 917 -19.293019 -25.32584 -19.293019 -6.032821327 6.03282133 15.53267584 #> 918 -40.170858 -40.29967 -40.170858 -0.128816294 0.12881629 -5.34516256 #> 919 -30.619475 -32.53209 -30.619475 -1.912613364 1.91261336 4.20622021 #> 920 -32.831904 -33.44078 -32.831904 -0.608876050 0.60887605 1.99379145 #> 921 -46.948254 -33.40729 -33.407286 13.540968028 0.00000000 1.41840927 #> 922 -13.170228 -31.26175 -13.170228 -18.091521986 18.09152199 21.65546707 #> 923 -31.089490 -25.32508 -25.325084 5.764406748 0.00000000 9.50061179 #> 924 -29.535986 -40.37809 -29.535986 -10.842102776 10.84210278 5.28970973 #> 925 -46.770086 -56.78714 -46.770086 -10.017058723 10.01705872 -11.94439034 #> 926 -37.877645 -43.84422 -37.877645 -5.966577482 5.96657748 -3.05194927 #> 927 -21.609433 -28.17352 -21.609433 -6.564089933 6.56408993 13.21626195 #> 928 -29.350343 -22.34239 -22.342385 7.007957804 0.00000000 12.48331008 #> 929 -22.581503 -26.80641 -22.581503 -4.224904307 4.22490431 12.24419193 #> 930 -19.929154 -20.89432 -19.929154 -0.965165211 0.96516521 14.89654095 #> 931 -35.853191 -34.15562 -34.155619 1.697572041 0.00000000 0.67007669 #> 932 -29.280579 -35.66376 -29.280579 -6.383183587 6.38318359 5.54511583 #> 933 -42.782708 -37.12598 -37.125975 5.656732830 0.00000000 -2.30028011 #> 934 -64.398227 -52.68949 -52.689491 11.708735710 0.00000000 -17.86379610 #> 935 -54.375214 -43.86415 -43.864145 10.511068979 0.00000000 -9.03845001 #> 936 -31.105217 -38.24942 -31.105217 -7.144200109 7.14420011 3.72047868 #> 937 -57.280995 -45.58849 -45.588489 11.692505324 0.00000000 -10.76279393 #> 938 -20.314048 -30.83581 -20.314048 -10.521760053 10.52176005 14.51164695 #> 939 -47.717675 -40.38705 -40.387050 7.330624702 0.00000000 -5.56135490 #> 940 -35.017103 -31.77475 -31.774748 3.242355139 0.00000000 3.05094754 #> 941 -28.972912 -37.92054 -28.972912 -8.947624724 8.94762472 5.85278370 #> 942 -61.709263 -41.83632 -41.836323 19.872940260 0.00000000 -7.01062734 #> 943 -28.498268 -26.42468 -26.424679 2.073589096 0.00000000 8.40101680 #> 944 -23.476430 -32.77582 -23.476430 -9.299391160 9.29939116 11.34926577 #> 945 -24.839448 -33.38054 -24.839448 -8.541094674 8.54109467 9.98624705 #> 946 -71.360195 -53.39980 -53.399797 17.960397900 0.00000000 -18.57410164 #> 947 -31.257565 -30.87163 -30.871630 0.385935137 0.00000000 3.95406509 #> 948 -46.666844 -35.06900 -35.069004 11.597839166 0.00000000 -0.24330904 #> 949 -33.773768 -35.18319 -33.773768 -1.409423485 1.40942349 1.05192776 #> 950 -49.664049 -41.50245 -41.502447 8.161601836 0.00000000 -6.67675168 #> 951 -25.020204 -24.96256 -24.962563 0.057640900 0.00000000 9.86313265 #> 952 -50.784434 -35.57488 -35.574881 15.209553481 0.00000000 -0.74918558 #> 953 -47.133325 -44.79773 -44.797729 2.335595875 0.00000000 -9.97203366 #> 954 -15.195139 -18.65556 -15.195139 -3.460424906 3.46042491 19.63055652 #> 955 -58.959383 -50.64467 -50.644665 8.314717988 0.00000000 -15.81896982 #> 956 -37.131031 -39.17107 -37.131031 -2.040035839 2.04003584 -2.30533537 #> 957 -24.064513 -22.93854 -22.938544 1.125969273 0.00000000 11.88715117 #> 958 -15.834385 -23.42056 -15.834385 -7.586177171 7.58617717 18.99130994 #> 959 -30.307759 -29.21873 -29.218734 1.089024986 0.00000000 5.60696113 #> 960 -31.958113 -29.84855 -29.848549 2.109564410 0.00000000 4.97714682 #> 961 -28.570159 -36.18767 -28.570159 -7.617513223 7.61751322 6.25553619 #> 962 -42.529496 -37.83789 -37.837891 4.691604961 0.00000000 -3.01219544 #> 963 -51.352502 -41.09164 -41.091641 10.260860948 0.00000000 -6.26594538 #> 964 -35.772453 -26.09408 -26.094077 9.678376352 0.00000000 8.73161873 #> 965 -21.182119 -24.31967 -21.182119 -3.137546837 3.13754684 13.64357621 #> 966 -41.536898 -36.11154 -36.111537 5.425361506 0.00000000 -1.28584137 #> 967 -45.802594 -38.16879 -38.168789 7.633805431 0.00000000 -3.34309351 #> 968 -31.060313 -28.26441 -28.264414 2.795898975 0.00000000 6.56128170 #> 969 -31.543288 -28.71105 -28.711052 2.832236136 0.00000000 6.11464341 #> 970 -47.005711 -47.06168 -47.005711 -0.055971604 0.05597160 -12.18001586 #> 971 -43.348800 -50.27717 -43.348800 -6.928372094 6.92837209 -8.52310477 #> 972 -18.095025 -21.23151 -18.095025 -3.136485082 3.13648508 16.73067066 #> 973 -37.093331 -36.21202 -36.212021 0.881310055 0.00000000 -1.38632569 #> 974 -20.436049 -22.96945 -20.436049 -2.533403030 2.53340303 14.38964663 #> 975 -51.468477 -38.33349 -38.333485 13.134992288 0.00000000 -3.50778971 #> 976 -29.045457 -30.49799 -29.045457 -1.452534256 1.45253426 5.78023794 #> 977 -28.464792 -40.71263 -28.464792 -12.247837324 12.24783732 6.36090300 #> 978 -40.064048 -31.25327 -31.253271 8.810776482 0.00000000 3.57242398 #> 979 -23.098403 -26.82179 -23.098403 -3.723391350 3.72339135 11.72729237 #> 980 -24.156203 -24.90490 -24.156203 -0.748701646 0.74870165 10.66949266 #> 981 -18.176479 -22.35662 -18.176479 -4.180139211 4.18013921 16.64921596 #> 982 -27.210469 -31.21309 -27.210469 -4.002620995 4.00262100 7.61522651 #> 983 -56.970749 -54.47260 -54.472596 2.498153118 0.00000000 -19.64690070 #> 984 -32.078136 -35.10138 -32.078136 -3.023245302 3.02324530 2.74755926 #> 985 -42.039296 -36.08732 -36.087318 5.951977416 0.00000000 -1.26162316 #> 986 -38.579652 -36.05915 -36.059150 2.520502260 0.00000000 -1.23345433 #> 987 -24.653947 -35.08838 -24.653947 -10.434429882 10.43442988 10.17174875 #> 988 -30.105686 -38.72078 -30.105686 -8.615091202 8.61509120 4.72000927 #> 989 -55.837030 -44.00264 -44.002638 11.834392744 0.00000000 -9.17694241 #> 990 -58.380768 -51.32742 -51.327420 7.053348118 0.00000000 -16.50172487 #> 991 -36.079700 -29.23238 -29.232384 6.847316399 0.00000000 5.59331151 #> 992 -30.576011 -28.39595 -28.395946 2.180064907 0.00000000 6.42974933 #> 993 -16.767220 -23.58544 -16.767220 -6.818216019 6.81821602 18.05847571 #> 994 -89.942217 -60.93182 -60.931819 29.010397336 0.00000000 -26.10612395 #> 995 -46.337516 -47.47181 -46.337516 -1.134293175 1.13429317 -11.51182060 #> 996 -63.170017 -49.02473 -49.024726 14.145290909 0.00000000 -14.19903034 #> 997 -23.653379 -44.83142 -23.653379 -21.178036470 21.17803647 11.17231593 #> 998 -21.476949 -31.85681 -21.476949 -10.379865068 10.37986507 13.34874585 #> 999 -26.663848 -24.42500 -24.425002 2.238845425 0.00000000 10.40069284 #> 1000 -35.234486 -32.59782 -32.597825 2.636661457 0.00000000 2.22787079 #> Average -36.054123 -34.82570 -32.411228 1.228428062 2.41446695 2.41446695 #> #> $names.cols #> [1] \"U1\" \"U2\" \"U*\" \"IB2_1\" \"OL\" \"VI\" #> #> $wtp #> [1] 25000 #> #> $ind.table #> [1] 251 #>"},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":null,"dir":"Reference","previous_headings":"","what":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","text":"data set contains results Bayesian analysis used model clinical output costs associated health economic evaluation four different smoking cessation interventions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","text":"data list including variables needed smoking cessation cost-effectiveness analysis. variables follows: list(\"cost\") matrix 500 simulations posterior distribution overall costs associated four strategies list(\"data\") dataset containing characteristics smokers UK population list(\"eff\") matrix 500 simulations posterior distribution clinical benefits associated four strategies list(\"life.years\") matrix 500 simulations posterior distribution life years gained strategy list(\"pi_post\") matrix 500 simulations posterior distribution event smoking cessation strategy list(\"smoking\") data frame containing inputs needed network meta-analysis model. data.frame object contains: nobs: record ID number, s: study ID number, : intervention ID number, r_i: number patients quit smoking, n_i: total number patients row-specific arm b_i: reference intervention study list(\"smoking_mat\") matrix obtained running network meta-analysis model based data contained smoking object list(\"treats\") vector labels associated four strategies","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","text":"Effectiveness data adapted Hasselblad V. (1998). Meta-analysis Multitreatment Studies. Medical Decision Making 1998;18:37-43. Cost population characteristics data adapted various sources: Taylor, D.H. Jr, et al. (2002). Benefits smoking cessation longevity. American Journal Public Health 2002;92(6) ASH: Action Smoking Health (2013). ASH fact sheet smoking statistics, https://ash.org.uk/files/documents/ASH_106.pdf Flack, S., et al. (2007). Cost-effectiveness interventions smoking cessation. York Health Economics Consortium, January 2007 McGhan, W.F.D., Smith, M. (1996). Pharmacoeconomic analysis smoking-cessation interventions. American Journal Health-System Pharmacy 1996;53:45-52","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions — Smoking","text":"Baio G. (2012). Bayesian Methods Health Economics. CRC/Chapman Hall, London","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":null,"dir":"Reference","previous_headings":"","what":"Structural Probability Sensitivity Analysis — struct.psa","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"Computes weights associated set competing models order perform structural PSA.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"","code":"struct.psa( models, effect, cost, ref = NULL, interventions = NULL, Kmax = 50000, plot = FALSE, w = NULL )"},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"models list containing output either R2jags R2WinBUGS models need combined model average effect list containing measure effectiveness computed various models (one matrix n.sim x n.ints simulations model) cost list containing measure costs computed various models (one matrix n.sim x n.ints simulations model) ref intervention considered reference strategy. default value ref=1 means intervention appearing first reference (s) () comparator(s) interventions Defines labels associated intervention. default NULL, assigns labels form \"Intervention1\", ... , \"InterventionT\" Kmax Maximum value willingness pay considered. Default value 50000. willingness pay approximated discrete grid interval [0, Kmax]. grid equal k parameter given, composed 501 elements k=NULL (default) plot logical value indicating whether function produce summary plot w vector weights. default NULL indicate function calculate model weights based DIC individual model fit. behaviour can overridden passing vector w, instance based expert opinion","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"List object bcea object, model weights DIC","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"model list containing output either R2jags R2WinBUGS models need combined model average effect list containing measure effectiveness computed various models (one matrix n_sim x n_ints simulations model) cost list containing measure costs computed various models (one matrix n_sim x n_ints simulations model).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Structural Probability Sensitivity Analysis — struct.psa","text":"","code":"if (FALSE) { # load sample jags output load(system.file(\"extdata\", \"statins_base.RData\", package = \"BCEA\")) load(system.file(\"extdata\", \"statins_HC.RData\", package = \"BCEA\")) interventions <- c(\"Atorvastatin\", \"Fluvastatin\", \"Lovastatin\", \"Pravastatin\", \"Rosuvastatin\", \"Simvastatin\") m1 <- bcea(eff = statins_base$sims.list$effect, cost = statins_base$sims.list$cost.tot, ref = 1, interventions = interventions) m2 <- bcea(eff = statins_HC$sims.list$effect, cost = statins_HC$sims.list$cost.tot, ref = 1, interventions = interventions) models <- list(statins_base, statins_HC) effects <- list(statins_base$sims.list$effect, statins_HC$sims.list$effect) costs <- list(statins_base$sims.list$cost.tot, statins_HC$sims.list$cost.tot) m3 <- struct.psa(models, effects, costs, ref = 1, interventions = interventions) }"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for Objects of Class bcea — summary.bcea","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"Produces table printout summary results health economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"","code":"# S3 method for bcea summary(object, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"object bcea object containing results Bayesian modelling economic evaluation. wtp value willingness pay threshold used summary table. ... Additional arguments affecting summary produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"Prints summary table information health economic output synthetic information economic measures (EIB, CEAC, EVPI).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for Objects of Class bcea — summary.bcea","text":"","code":"data(Vaccine) he <- bcea(eff, cost, interventions = treats, ref = 2) summary(he) #> #> Cost-effectiveness analysis summary #> #> Reference intervention: Vaccination #> Comparator intervention: Status Quo #> #> Optimal decision: choose Status Quo for k < 20100 and Vaccination for k >= 20100 #> #> #> Analysis for willingness to pay parameter k = 25000 #> #> Expected net benefit #> Status Quo -36.054 #> Vaccination -34.826 #> #> EIB CEAC ICER #> Vaccination vs Status Quo 1.2284 0.529 20098 #> #> Optimal intervention (max expected net benefit) for k = 25000: Vaccination #> #> EVPI 2.4145"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"Prints summary table results mixed analysis economic evaluation given model.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"","code":"# S3 method for mixedAn summary(object, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"object object class mixedAn, results function mixedAn(), generating economic evaluation set interventions, considering given market shares option. wtp value willingness pay chosen present analysis. ... Additional arguments affecting summary produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"Produces table summary information loss expected value information generated inclusion non cost-effective interventions market.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"Baio G, Russo P (2009). “decision-theoretic framework application cost-effectiveness analysis regulatory processes.” Pharmacoeconomics, 27(8), 5--16. ISSN 20356137, doi:10.1007/bf03320526 . Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) — summary.mixedAn","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # Load the processed results of the MCMC simulation model data(Vaccine) # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000 # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) ) mixedAn(m) <- NULL # uses the results of the mixed strategy # analysis (a \"mixedAn\" object) # the vector of market shares can be defined # externally. If NULL, then each of the T # interventions will have 1/T market share # Prints a summary of the results summary(m, # uses the results of the mixed strategy analysis wtp=25000) # (a \"mixedAn\" object) #> #> Analysis of mixed strategy for willingness to pay parameter k = 25000 #> #> Reference intervention: Vaccination (50.00% market share) #> Comparator intervention: Status Quo (50.00% market share) #> #> Loss in the expected value of information = 0.61 #> # selects the relevant willingness to pay # (default: 25,000)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for Objects of Class pairwise — summary.pairwise","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"Produces table printout summary results health economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"","code":"# S3 method for pairwise summary(object, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"object pairwise object containing results Bayesian modelling economic evaluation. wtp value willingness pay threshold used summary table. ... Additional arguments affecting summary produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"Prints summary table information health economic output synthetic information economic measures (EIB, CEAC, EVPI).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for Objects of Class pairwise — summary.pairwise","text":"","code":"data(Vaccine) he <- bcea(eff, cost, interventions = treats, ref = 2) he_multi <- multi.ce(he) summary(he_multi) #> #> Cost-effectiveness analysis summary #> #> Intervention(s): Status Quo #> : Vaccination #> #> Optimal decision: choose Status Quo for k < 20100 and Vaccination for k >= 20100 #> #> #> Analysis for willingness to pay parameter k = 25000 #> #> Expected net benefit EIB CEAC ICER #> Status Quo -36.054 NA 0.471 NA #> Vaccination -34.826 NA 0.529 NA #> #> Optimal intervention (max expected net benefit) for k = 25000: Vaccination #> #> EVPI 2.4145"},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Dataset For ICERs From bcea Object — tabulate_means","title":"Calculate Dataset For ICERs From bcea Object — tabulate_means","text":"Calculate Dataset ICERs bcea Object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Dataset For ICERs From bcea Object — tabulate_means","text":"","code":"tabulate_means(he, comp_label = NULL, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Dataset For ICERs From bcea Object — tabulate_means","text":"bcea object containing results Bayesian modelling economic evaluation. comp_label Optional vector strings comparison labels ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Dataset For ICERs From bcea Object — tabulate_means","text":"data.frame object including mean outcomes, comparison identifier, comparison label associated ICER","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Dataset For ICERs From bcea Object — tabulate_means","text":"","code":"data(\"Smoking\") he <- BCEA::bcea(eff, cost) #> No reference selected. Defaulting to first intervention. tabulate_means(he) #> lambda.e lambda.c comparison label ICER #> intervention 2 -0.2882396 -45.73316 1 1 158.6637 #> intervention 3 -0.4848577 -94.91904 2 2 195.7668 #> intervention 4 -0.7225198 -143.30076 3 3 198.3347"},{"path":"https://n8thangreen.github.io/BCEA/reference/theme_bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"bcea theme ggplot2 — theme_bcea","title":"bcea theme ggplot2 — theme_bcea","text":"bcea theme ggplot2","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/theme_bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bcea theme ggplot2 — theme_bcea","text":"","code":"theme_default() theme_ceac() theme_ceplane() theme_eib() theme_contour()"},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":null,"dir":"Reference","previous_headings":"","what":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","text":"data set contains results Bayesian analysis used model clinical output costs associated influenza vaccination.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","text":"data list including variables needed influenza vaccination. variables follows: list(\"cost\") matrix simulations posterior distribution overall costs associated two treatments list(\"c.pts\") list(\"cost.GP\") matrix simulations posterior distribution costs GP visits associated two treatments list(\"cost.hosp\") matrix simulations posterior distribution costs hospitalisations associated two treatments list(\"cost.otc\") matrix simulations posterior distribution costs --counter medications associated two treatments list(\"cost.time.\") matrix simulations posterior distribution costs time work associated two treatments list(\"cost.time.vac\") matrix simulations posterior distribution costs time needed get vaccination associated two treatments list(\"cost.travel\") matrix simulations posterior distribution costs travel get vaccination associated two treatments list(\"cost.trt1\") matrix simulations posterior distribution overall costs first line treatment associated two interventions list(\"cost.trt2\") matrix simulations posterior distribution overall costs second line treatment associated two interventions list(\"cost.vac\") matrix simulations posterior distribution costs vaccination list(\"eff\") matrix simulations posterior distribution clinical benefits associated two treatments list(\"e.pts\") list(\"N\") number subjects reference population list(\"N.outcomes\") number clinical outcomes analysed list(\"N.resources\") number health-care resources study list(\"QALYs.adv\") vector posterior distribution QALYs associated advert events list(\"QALYs.death\") vector posterior distribution QALYs associated death list(\"QALYs.hosp\") vector posterior distribution QALYs associated hospitalisation list(\"QALYs.inf\") vector posterior distribution QALYs associated influenza infection list(\"QALYs.pne\") vector posterior distribution QALYs associated pneumonia list(\"treats\") vector labels associated two treatments list(\"vaccine_mat\") matrix containing simulations parameters used original model","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","text":"Adapted Turner D, Wailoo , Cooper N, Sutton , Abrams K, Nicholson K. cost-effectiveness influenza vaccination healthy adults 50-64 years age. Vaccine. 2006;24:1035-1043.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination — Vaccine","text":"Baio, G., Dawid, . P. (2011). Probabilistic Sensitivity Analysis Health Economics. Statistical Methods Medical Research doi:10.1177/0962280211419832.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate bcea — validate_bcea","title":"Validate bcea — validate_bcea","text":"Validate bcea","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate bcea — validate_bcea","text":"","code":"validate_bcea(eff, cost, ref, interventions)"},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate bcea — validate_bcea","text":"eff Effectiveness matrix cost Cost matrix ref Reference intervention interventions interventions","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate EIB parameters — validate_eib_params","title":"Validate EIB parameters — validate_eib_params","text":"Validate EIB parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate EIB parameters — validate_eib_params","text":"","code":"validate_eib_params(params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate EIB parameters — validate_eib_params","text":"params Graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate EIB parameters — validate_eib_params","text":"List graph parameters","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-246","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.6","title":"BCEA 2.4.6","text":"February 2024 Patch fixing small bugs last CRAN release. Converted help documentation man-roxygen folder md (cf858b1) bugfix: line width CEAC plot. ggplot2 changed version 3 linewidth size argument changed code. Updated scale_linewidth_manual(). (60bea9c) Using testdata folder testthat unit tests. (cbce0fa)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-245","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.5","title":"BCEA 2.4.5","text":"November 2023 Moved internal EVPPI calculation BCEA now uses voi package instead. Refactoring retaining interface functionality. Latest version voi needed use check = TRUE voi::evppi() order access fitting data (6e436b5, 94f5fc5) evppi() tested use cases BCEA book (1c1457d2) Select parameters position (well name) new evppi() (f2e4d005) Use single parameter case like voi package methods sal (#140) New evppi() matching output old evppi() (1e2c5e7) Latest development version voi needed use check = TRUE voi::evppi() order access fitting data (6e436b5, 94f5fc5) longer require INLA package available inside BCEA can remove direct dependency. helps passing CRAN checks GitHub Actions ()","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-244","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.4","title":"BCEA 2.4.4","text":"June 2023 Patch fix CRAN checks error. Suggested package MCMCvis wasn’t used conditionally unit test. Moved Required packages DESCRIPTION.","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-243","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.3","title":"BCEA 2.4.3","text":"May 2023","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bug-fixes-2-4-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"BCEA 2.4.3","text":"Consistent colours across plots intervention grid plots plot.bcea() (cf1ee43) make.report() change variable name (f940f2e) Fixed issue summary table names interventions wrong order (6a006e3) summary.bcea() now prints results chosen comparisons always . kstar best bcea() object updated subset interventions (#125)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"refactoring-2-4-3","dir":"Changelog","previous_headings":"","what":"Refactoring","title":"BCEA 2.4.3","text":"withr::with_par() used plotting function plot.bcea() temporarily change graphics parameters. (725c536) Using @md markdown syntax function documentation Update psa.struct() add absolute value formula compute weights (1cea278) Use dplyr piping new syntax .data$* simply using speech marks \"*\" (2b280ad)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"miscellaneous-2-4-3","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"BCEA 2.4.3","text":"Template added GitHub Issues (0ea59fa)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-242","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.2","title":"BCEA 2.4.2","text":"August 2022","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bug-fixes-2-4-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"BCEA 2.4.2","text":"summary.bcea() wasn’t passing wtp argument sim_table() internally (5440eb3) summary() basic bcea multi.ce objects. Now summary.pairwise() method. (88ade51) struct.psa() output now works summary() plots still work without use $ get bcea object . (b014c83) Changed wtp argument bcea() k wtp plotting functions refers wtp line scalar whereas k grid points. Added error message use new argument. (b014c83) bcea() still allows scalar k added warning give empty plots. Updated GitHub Actions checking package use r-lib/Actions version 2. error finding INLA solved Gabor RStudio (see thread https://community.rstudio.com/t/-finding-inla-package---cran--actions/141398) GrassmannOptim package r-release-macos-x86_64 isn’t available resulting CRAN check error doesn’t appear maintained. Tried emailing author bounced. Removed dependency copied GrassmannOptim() function inside package acknowledgement.","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"refactoring-2-4-2","dir":"Changelog","previous_headings":"","what":"Refactoring","title":"BCEA 2.4.2","text":"Now uses Rdpack bibliography documentation (229c96d) cost health values Smoking Vaccine data sets renamed c e cost eff. avoid conflict c() function. Changed axes labels cost-effectiveness planes “differential” “incremental”. (688d98b)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"new-features-2-4-2","dir":"Changelog","previous_headings":"","what":"New features","title":"BCEA 2.4.2","text":"Can now specify order interventions labels legend ce plane (contour plots) base R ggplot2 .e. reference first second optional ref_first argument (cc38f07) Can specify currency axes ceplane.plot() ceac.plot() ggplot2 versions (6808aa6) Argument added ceplane.plot() icer_annot annotate ICER points text label intervention name. ggplot2 moment. (a7b4beb) Added pos argument contour2() consistent contour() ceplane.plot(). (50f8f8b) Allow passing ref argument name well index bcea(). (9eab459)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-2412","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.1.2","title":"BCEA 2.4.1.2","text":"April 2022","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bug-fixes-2-4-1-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"BCEA 2.4.1.2","text":"ceplane_ggplot() missing legend Legend bug evppi() Arguments consistent order ceplane.plot() ceplane_plot_base() wasn’t showing grey area. Fixed removing alpha transparency ceac.plot() wasn’t showing confidence interval default one comparison Typo fixed dropping dimension compute_vi() setReferenceGroup() CEAC plot legend error; doesn’t use supplied names generic intervention 1, intervention 2, … (#82) Missing multi.ce() line reference group (#80)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"miscellaneous-2-4-1-2","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"BCEA 2.4.1.2","text":"Use cli package warning messages Removed internal helper functions Manual using @keyword internal Refactor contour plots Extend function (ceac.plot()) take style arguments e.g. colour lines, types points line thickness. Resuse ceplane.plot() code contour() line length, seq_len(), remove ; Contributor guidelines (#93) Deprecated functions document contour2() changed xlim, ylim arguments optional; ceplane.plot() since passes contour() ceplane.plot() vignettes written","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-2411","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.1.1","title":"BCEA 2.4.1.1","text":"Oct 2021","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"major-refactoring-2-4-1-1","dir":"Changelog","previous_headings":"","what":"Major refactoring","title":"BCEA 2.4.1.1","text":"Code base improved robustness extensibility. bcea() now helper function calls constructor new_bcea(), separating concerns. new_bcea() composed smaller HEE statistics functions names starting compute_* e.g. compute_CEAC(), compute_EIB(),…. allows us call test individually. also allows flexibility changing adding functionality new_bcea(). Plotting functions rewritten. functions now simply dispatch base R, ggplot2 plotly versions (think strategy pattern). Internally, functions, e.g.ceplane_plot_ggplot(), also split parameter data setting plotting components. modulisation allows us add new layers plots modify existing parameter sets defaults. also return data without plotting step e.g. ggplot2::autoplot(). also means can reuse functionality across plots axes legend setting e.g. BCEA:::where_legend(). Deprecated mce.plot(). Now dispatched ceac.plot() multi.ce() bcea() outputs. multiple comparison plot pairwise comparison interventions returned default. alternative version comparison reference group still available. Plots tables using S3 methods bcea type object. Tables updated. Duplication summary() sim_table() removed. createInputs() used EVPI calculation now dispatches S3 methods JAGS, BUGS, Stan R data types. make.report() rewritten separate section files.","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"new-features-2-4-1-1","dir":"Changelog","previous_headings":"","what":"New features","title":"BCEA 2.4.1.1","text":"Extend ways set comparison interventions. Subsets comparison can still set call plotting function . Now subsets can set original bcea() construction separately using setter functions setComparisons(). Similarly, maximum willingness pay reference group can set setKmax() setReferenceGroup(), respectively. multi.ce() CEriskAv() also now work similarly. operate modifying bcea object, rather creating new one (think decorator pattern). bcea() methods JAGS, WinBUGS, Stan (#76)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"miscellaneous-2-4-1-1","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"BCEA 2.4.1.1","text":"Additional help documentation examples. New vignettes plotting comparison intervention setting. Testing suite started. comprehensive yet. Added NEWS.md file track changes package. Details previous releases, dates, versions, fixes enhancements obtained CRAN code comments little patchy. pkgdown GitHub site made. Cheatsheet written published RStudio site (#22). Dependency package ldr removed BCEA removed CRAN (#74)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-23-11","dir":"Changelog","previous_headings":"","what":"BCEA 2.3-1.1","title":"BCEA 2.3-1.1","text":"26 Aug 2019","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-23-1","dir":"Changelog","previous_headings":"","what":"BCEA 2.3-1","title":"BCEA 2.3-1","text":"5 Aug 2019","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-6","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-6","title":"BCEA 2.2-6","text":"11 July 2018 Fix evppi allow N selected methods Fix diag.evppi","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-5","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-5","title":"BCEA 2.2-5","text":"18 Nov 2016 changes EVPPI","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-224","dir":"Changelog","previous_headings":"","what":"BCEA 2.2.4","title":"BCEA 2.2.4","text":"Nov 2016 Fixes new ggplot2 version (legend.spacing() plot.title hjust argument)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-3","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-3","title":"BCEA 2.2-3","text":"22 May 2016 Major update EVPPI include PFC Fixed issues info.rank","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-2","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-2","title":"BCEA 2.2-2","text":"25 Jan 2016 Minor change ceef.plot align ggplot2 v2.0.0","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-221","dir":"Changelog","previous_headings":"","what":"BCEA 2.2.1","title":"BCEA 2.2.1","text":"Oct 2015 Adds info-rank plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22","dir":"Changelog","previous_headings":"","what":"BCEA 2.2","title":"BCEA 2.2","text":"Oct 2015 Cleaned aligned R’s settings EVPPI function polished ","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-21-1","dir":"Changelog","previous_headings":"","what":"BCEA 2.1-1","title":"BCEA 2.1-1","text":"6 May 2015 2015 New function EVPPI using SPDE-INLA Modifications EVPPI functions Documentation updated Allows xlim & ylim ceplane.plot(), contour() contour2() functions now possible run bcea scalar wtp Old evppi() function method renamed evppi0, means ’s also new plot.evppi0 method","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-21-0","dir":"Changelog","previous_headings":"","what":"BCEA 2.1-0","title":"BCEA 2.1-0","text":"13 Jan 2015 Migrated (require()) (requireNamespace(,quietly=TRUE)) Documentation updated Added threshold argument ceef.plot function","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-210-pre2","dir":"Changelog","previous_headings":"","what":"BCEA 2.1.0-pre2","title":"BCEA 2.1.0-pre2","text":"Oct 2014 modifications ceef.plot, createInputs, struct.psa","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-21-0-pre1","dir":"Changelog","previous_headings":"","what":"BCEA 2.1-0-pre1","title":"BCEA 2.1-0-pre1","text":"13 Jan 2015 Documentation updated Smoking dataset ceef.plot function included, additional modifications","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20-2c","dir":"Changelog","previous_headings":"","what":"BCEA 2.0-2c","title":"BCEA 2.0-2c","text":"2 Dec 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-v20-2b","dir":"Changelog","previous_headings":"","what":"BCEA v2.0-2b","title":"BCEA v2.0-2b","text":"2 Dec 2013 ceac.plot eib.plot: option comparison included base graphics","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20-2","dir":"Changelog","previous_headings":"","what":"BCEA 2.0-2","title":"BCEA 2.0-2","text":"2 Dec 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20-1","dir":"Changelog","previous_headings":"","what":"BCEA 2.0-1","title":"BCEA 2.0-1","text":"31 July 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20","dir":"Changelog","previous_headings":"","what":"BCEA 2.0","title":"BCEA 2.0","text":"30 July 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"feature-updates-2-0","dir":"Changelog","previous_headings":"","what":"Feature updates","title":"BCEA 2.0","text":"Implements two quick general methods compute EVPPI Function CreateInputs(), takes input object class rjags bugs Compute EVPPI one parameters calling function evppi() Results can visualised using specific method plot class evppi show overall EVPI EVPPI selected parameter(s)","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-13-0","dir":"Changelog","previous_headings":"","what":"BCEA 1.3-0","title":"BCEA 1.3-0","text":"3 July 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-12","dir":"Changelog","previous_headings":"","what":"BCEA 1.2","title":"BCEA 1.2","text":"17 September 2012","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-111","dir":"Changelog","previous_headings":"","what":"BCEA 1.1.1","title":"BCEA 1.1.1","text":"22 Feb 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-11","dir":"Changelog","previous_headings":"","what":"BCEA 1.1","title":"BCEA 1.1","text":"15 Sept 2012","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-10","dir":"Changelog","previous_headings":"","what":"BCEA 1.0","title":"BCEA 1.0","text":"13 May 2012","code":""}]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
index 696e8baf..0efc2a45 100644
--- a/docs/sitemap.xml
+++ b/docs/sitemap.xml
@@ -327,6 +327,9 @@
https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html
+
+ https://n8thangreen.github.io/BCEA/reference/get_fitted_.html
+
https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html
diff --git a/tests/testthat/test-evppi.R b/tests/testthat/test-evppi.R
index 2e185d13..17cb17a7 100644
--- a/tests/testthat/test-evppi.R
+++ b/tests/testthat/test-evppi.R
@@ -7,6 +7,12 @@
# library(BCEA)
if (interactive()) library(testthat)
+if (!requireNamespace("voi", quietly = TRUE)) {
+ stop(
+ "Package \"voi (>= 1.0.1)\" must be installed to use this function.",
+ call. = FALSE
+ )
+}
test_that("GAM regression (default) with vaccine data", {