diff --git a/404.html b/404.html index 6f86357..16e14c5 100644 --- a/404.html +++ b/404.html @@ -46,7 +46,7 @@ - github + diff --git a/authors.html b/authors.html index 6774c52..389158e 100644 --- a/authors.html +++ b/authors.html @@ -28,7 +28,7 @@ - github + diff --git a/index.html b/index.html index 73c6079..027096c 100644 --- a/index.html +++ b/index.html @@ -48,7 +48,7 @@ - github + diff --git a/pkgdown.yml b/pkgdown.yml index ab45504..e1e8f84 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 2.19.2 pkgdown: 2.0.7 pkgdown_sha: ~ articles: {} -last_built: 2023-01-23T22:44Z +last_built: 2023-01-23T22:47Z urls: reference: https://pinduzera.github.io/genlogis/reference article: https://pinduzera.github.io/genlogis/articles diff --git a/reference/distrib.html b/reference/distrib.html index 1a1f2f7..3547fac 100644 --- a/reference/distrib.html +++ b/reference/distrib.html @@ -28,7 +28,7 @@ - github + @@ -120,26 +120,23 @@

Examples rgenlog(100) -#> [1] -1.5973060780 1.8469333593 -1.1589975196 0.7593395529 -0.8282933574 -#> [6] -1.8189683961 0.8105236822 -0.5416872399 -1.2004121484 -0.9798103799 -#> [11] -0.9785022696 1.5691416772 -0.8603533364 1.3809453014 0.9204901035 -#> [16] 0.5239731763 -2.3422762392 0.7325788894 -0.5478148459 -0.0599068774 -#> [21] 1.1901488702 1.1646961879 -0.0590565949 0.7806909828 -0.4647704850 -#> [26] -0.0075752774 -0.9243616691 1.0953170642 1.8512968540 1.3970418212 -#> [31] -0.2979343337 -0.7228095016 0.6628456534 1.2759491897 1.0976531692 -#> [36] 1.2129034791 0.5578051124 0.6007138546 -0.2601975014 0.8920486902 -#> [41] -1.3213149658 0.5028720514 -0.1446198286 0.1775284956 -1.1752357646 -#> [46] -0.7879790879 0.7420850211 1.4842216037 -1.5792873026 0.3247211167 -#> [51] 0.8404622028 0.9210866354 -1.4006066761 0.5707456255 0.8726364405 -#> [56] -0.6964009210 -1.4182408425 -2.0657956884 1.1266853847 0.0647920253 -#> [61] 0.7163431290 -0.0245650463 0.8370138896 0.9447948272 0.7990404726 -#> [66] 0.9164235667 0.3023775397 -1.2655023258 1.1580985640 1.1146871184 -#> [71] 0.3749429089 -2.0074099808 1.9613052633 0.4379508999 -0.5265390481 -#> [76] 0.5595450199 0.8498043840 -0.9639220819 1.4336129057 -1.4662492396 -#> [81] -0.2897429318 -0.8789979462 0.0008573161 0.1539664227 0.0910493076 -#> [86] -0.5478950080 -0.4053581283 -0.7279654074 0.6357919217 -0.8118998328 -#> [91] -0.5246200879 0.8736504377 -0.6128714069 1.0606306506 0.9817465941 -#> [96] 0.9815781244 1.3703118892 -0.0453467373 -0.9027165981 -0.7590511416 +#> [1] -0.58487865 1.45535120 -1.46889615 -0.91101019 1.45349069 -0.87649678 +#> [7] -0.29796606 0.99159044 1.61993039 1.66014645 -1.12590837 -1.46037202 +#> [13] -0.13625403 -0.23910696 -1.28930842 -1.05411082 -0.51334350 -0.95719013 +#> [19] 0.21603638 1.50837936 -0.12487832 0.72052045 1.47564446 -0.09202933 +#> [25] 0.58947070 -1.16644053 0.75791848 -1.13376197 1.35714271 1.55459351 +#> [31] 1.66513595 0.96534365 0.03704803 0.95015335 -1.63543670 0.83975884 +#> [37] 0.03679816 -1.16524523 -1.27374907 -1.11013582 1.32224149 -0.09695106 +#> [43] -1.41227725 -0.39517512 -0.20001973 1.19990649 -0.18538583 1.02946543 +#> [49] 0.14773808 -0.21632478 -0.55154153 -1.50913041 0.21984348 -0.75968639 +#> [55] 0.99350334 -1.24779420 1.23151962 -0.87781727 -0.52182090 -0.43726739 +#> [61] -0.58780588 1.30634364 -0.73319389 1.58363459 1.80285648 -1.32977683 +#> [67] -0.19253511 1.44026635 1.34154042 0.16363125 1.58408956 0.04192068 +#> [73] -0.87434205 1.75934912 1.49073946 1.22830632 0.29385024 -1.00137134 +#> [79] 0.13226600 -0.76136925 1.21006151 0.86227000 -1.27733886 -1.50443721 +#> [85] -0.12078951 -0.33474386 -1.22875006 -0.97050601 0.11804355 0.26191866 +#> [91] -0.64001558 -0.12807290 1.24580967 1.38185941 -0.69215165 1.27470863 +#> [97] 0.78891284 -0.70083512 0.54013419 -1.04995675 qgenlog(0.95) #> [1] 1.5143 diff --git a/reference/distrib_sk.html b/reference/distrib_sk.html index 57a6732..df3b9a4 100644 --- a/reference/distrib_sk.html +++ b/reference/distrib_sk.html @@ -28,7 +28,7 @@ - github + @@ -142,23 +142,23 @@

Examples rgenlog_sk(100) -#> [1] 0.43630494 1.29794730 0.61424154 -1.20661352 -1.25415854 -0.54876535 -#> [7] -1.46116518 0.83257457 1.06491590 0.46132542 0.73695417 -0.87584050 -#> [13] -0.07586695 -1.25184153 1.88944177 0.83462772 -0.25806080 2.06705313 -#> [19] 0.80927966 0.97483412 -0.50102668 1.11153506 -0.69216987 -0.72912284 -#> [25] -0.21230722 1.34561158 0.45184039 -1.22681883 1.00820083 0.79471736 -#> [31] 1.62184876 0.83017086 0.86769095 -0.46244725 1.40435763 0.38599960 -#> [37] -0.64575217 0.41766584 0.77813887 1.59745002 1.13344840 0.17419203 -#> [43] 0.01533669 -0.64057473 0.91545905 1.04898164 -1.38202626 0.63861411 -#> [49] -1.05467774 0.89451685 -1.05825910 -1.55684372 -0.24236910 1.13016193 -#> [55] -0.13850561 1.38241849 0.08245531 1.70420737 -1.41717486 -1.46252796 -#> [61] -0.15525420 1.16040219 -0.62310335 0.33236226 0.81362536 0.18724094 -#> [67] 1.13399050 1.02412511 -0.76918397 1.12161620 0.84858953 0.89537251 -#> [73] -1.09670707 -0.33633274 0.31212661 1.60794555 0.64991920 -1.46867244 -#> [79] 1.32086670 1.85665868 0.53129460 -0.77974607 1.39917444 -1.10554533 -#> [85] 1.42319366 2.14963000 0.76137122 -1.21063921 2.03836809 1.02185140 -#> [91] -0.92844392 -0.90145976 0.63249385 -0.18614750 -0.98244364 -1.25573682 -#> [97] 1.32067127 0.69659250 2.17324976 0.74555478 +#> [1] -0.77276500 1.37097793 -0.02268508 0.36851625 1.57153600 1.45826463 +#> [7] -0.55655663 0.18104925 1.38609156 -0.21696826 0.35434232 -1.59145325 +#> [13] -0.18135508 0.23780370 -0.24521378 -0.80661894 -0.64171041 1.22743907 +#> [19] 1.76726108 -0.60244154 0.10401794 -0.60142224 0.89111620 1.59353793 +#> [25] 1.01015221 1.01740622 1.66968588 1.14986251 2.03631266 -0.64071028 +#> [31] -1.22546490 -1.06311424 0.94468372 1.23886586 -1.18022493 0.94200612 +#> [37] -0.94759266 -0.15641882 1.17088219 -0.74178133 1.32119142 2.30336254 +#> [43] 0.42060173 1.18189933 1.07120702 -1.81927403 -1.31301868 1.51086021 +#> [49] 1.04843359 1.53032193 0.82217480 -0.38277004 1.07544115 -0.49334181 +#> [55] -0.81883176 0.34271773 0.79674170 0.66422579 1.06096462 0.71374336 +#> [61] 1.71960957 -1.31310191 -0.47382404 1.13969676 1.07412219 0.88818108 +#> [67] 1.09718896 -0.13889023 0.33079437 0.70049110 1.13088202 -1.56048760 +#> [73] -0.69649877 -1.30087661 0.19790769 0.88672828 0.79716549 -1.34966557 +#> [79] -0.07854816 1.06702341 -1.43640468 0.06418204 1.00582321 -0.47469418 +#> [85] 0.71631664 -1.40898063 -0.32527271 1.09208590 1.58187659 0.09131728 +#> [91] -1.28352662 1.33790357 0.93621750 0.09701173 0.55695498 -1.09307987 +#> [97] -0.44667644 1.58183228 1.59453987 1.65569265 qgenlog_sk(0.95) #> [1] 1.607905 diff --git a/reference/genlog_mle.html b/reference/genlog_mle.html index e2395ed..1dae829 100644 --- a/reference/genlog_mle.html +++ b/reference/genlog_mle.html @@ -28,7 +28,7 @@ - github + @@ -162,14 +162,14 @@

Examplesdatas <- rgenlog(10000, 1.5,2,2, 0) genlog_mle(c(.5,1.6, 1.5, 0),datas) #> $par -#> [1] 1.394682441 2.054909656 1.798232661 0.004956888 +#> [1] 1.473250136 2.044438004 1.907402254 -0.007229233 #> #> $value -#> [1] 8292.891 +#> [1] 8213.259 #> #> $counts #> function gradient -#> 65 15 +#> 65 16 #> #> $convergence #> [1] 0 @@ -181,7 +181,7 @@

Examples#> [1] 2 #> #> $barrier.value -#> [1] 0.0002248638 +#> [1] 0.0002160534 #> ## Select parameters starting values with genlog_slider @@ -196,14 +196,14 @@

Examples genlog_mle(parameters,datas) #> $par -#> [1] 1.523082700 2.024397847 2.062656142 -0.001228966 +#> [1] 1.462043387 2.047639808 1.985611585 -0.004823423 #> #> $value -#> [1] 8179.161 +#> [1] 8214.426 #> #> $counts #> function gradient -#> 38 7 +#> 39 7 #> #> $convergence #> [1] 0 @@ -215,7 +215,7 @@

Examples#> [1] 2 #> #> $barrier.value -#> [1] 0.0002048217 +#> [1] 0.0002110456 #> # } diff --git a/reference/genlog_mle_sk.html b/reference/genlog_mle_sk.html index 324e506..560a664 100644 --- a/reference/genlog_mle_sk.html +++ b/reference/genlog_mle_sk.html @@ -28,7 +28,7 @@ - github + @@ -164,14 +164,14 @@

Examplesdatas <- rgenlog_sk(10000, 0.3,0.9,1.5, 0, 0.9) genlog_mle_sk(c(0.3,0.9,1.5, 0, 0.9),datas) #> $par -#> [1] 0.291935207 0.913139043 1.461818162 -0.006611291 0.917229274 +#> [1] 0.298475326 0.904429619 1.482780468 -0.002193411 0.899402074 #> #> $value -#> [1] 10874.1 +#> [1] 10936.98 #> #> $counts #> function gradient -#> 45 8 +#> 48 7 #> #> $convergence #> [1] 0 @@ -183,7 +183,7 @@

Examples#> [1] 2 #> #> $barrier.value -#> [1] 0.000151263 +#> [1] 0.0001565813 #> ## Select parameters starting values with genlog_slider @@ -198,14 +198,14 @@

Examples genlog_mle_sk(parameters, datas) #> $par -#> [1] 1.573190648 1.870266416 2.149176935 0.004106287 -0.014384578 +#> [1] 1.5176416737 2.0333834227 2.0178149481 -0.0003373249 0.0038627704 #> #> $value -#> [1] 8334.158 +#> [1] 8176.155 #> #> $counts #> function gradient -#> 84 20 +#> 86 18 #> #> $convergence #> [1] 0 @@ -217,7 +217,7 @@

Examples#> [1] 2 #> #> $barrier.value -#> [1] 0.0002064364 +#> [1] 0.0002076098 #> # } diff --git a/reference/genlog_simu.html b/reference/genlog_simu.html index 3c579af..eab39c5 100644 --- a/reference/genlog_simu.html +++ b/reference/genlog_simu.html @@ -30,7 +30,7 @@ - github + diff --git a/reference/genlog_simu_sk.html b/reference/genlog_simu_sk.html index df40e23..2944dda 100644 --- a/reference/genlog_simu_sk.html +++ b/reference/genlog_simu_sk.html @@ -30,7 +30,7 @@ - github + diff --git a/reference/genlog_slider.html b/reference/genlog_slider.html index 4e4e7b4..7bf8625 100644 --- a/reference/genlog_slider.html +++ b/reference/genlog_slider.html @@ -28,7 +28,7 @@ - github + diff --git a/reference/index.html b/reference/index.html index 58f62c8..8f23ad0 100644 --- a/reference/index.html +++ b/reference/index.html @@ -28,7 +28,7 @@ - github + diff --git a/search.json b/search.json index 3dce30d..f5cecb7 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://pinduzera.github.io/genlogis/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Eduardo Hellas. Author, maintainer. Eduardo Monteiro. Author, contributor.","code":""},{"path":"https://pinduzera.github.io/genlogis/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hellas E, Monteiro E (2023). genlogis: Generalized Logistic Distribution. R package version 1.0.1, https://pinduzera.github.io/genlogis/.","code":"@Manual{, title = {genlogis: Generalized Logistic Distribution}, author = {Eduardo Hellas and Eduardo Monteiro}, year = {2023}, note = {R package version 1.0.1}, url = {https://pinduzera.github.io/genlogis/}, }"},{"path":"https://pinduzera.github.io/genlogis/index.html","id":"r-genlogis","dir":"","previous_headings":"","what":"Generalized Logistic Distribution","title":"Generalized Logistic Distribution","text":"package provides basic distribution functions generalized logistic distribution proposed Rathie Swamee (2006) https://www.rroij.com/open-access/-new-generalized-logistic-distributions--applicationsbarreto-fhs-mota-jma--rathie-pn-.pdf. also interactive ‘RStudio’ plot better guessing dynamically initial values ease included optimization simulating. build academic work University Brasília (UnB) supervision Dr. Eduardo Monteiro.","code":""},{"path":"https://pinduzera.github.io/genlogis/index.html","id":"installing","dir":"","previous_headings":"","what":"Installing","title":"Generalized Logistic Distribution","text":"","code":"# To install the package install.packages(\"genlogis\") # If you want the version from GitHub: # install.packages(\"devtools\") devtools::install_github('pinduzera/genlogis')"},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":null,"dir":"Reference","previous_headings":"","what":"The Generalized logistic distribution — distrib","title":"The Generalized logistic distribution — distrib","text":"Density, distribution function, quantile function random generation generalized logistic distribution.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Generalized logistic distribution — distrib","text":"","code":"pgenlog(q, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, lower.tail = TRUE) dgenlog(x, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0) qgenlog(k, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, lower.tail = TRUE) rgenlog(n, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0)"},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Generalized logistic distribution — distrib","text":", b, p parameters \\(\\ge 0\\), restrictions.* mu mu parameter lower.tail logical; TRUE (default), probabilities \\(P[X \\le x]\\) otherwise, \\(P[X > x]\\). x, q vector quantiles. k vector probabilities. n number observations. length(n) > 1, length taken number required","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Generalized logistic distribution — distrib","text":"dgenlog gives density, pgenlog gives distribution function, qgenlog gives quantile function, rgenlog generates random deviates. length result determined n rgenlog, maximum lengths numerical arguments functions.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Generalized logistic distribution — distrib","text":"used distribution package given : $$f(x) = ((+ b*(1+p)*(|x-mu|^p))*exp(-(x-mu)*(+b*(|x-mu|^p)))) / ((exp(-(x-mu)*(+ b* (|x-mu|^p)))+1)^2)$$ default values , b, p mu produces function mean 0 variance close 1. *Restrictions: p equals 0, b must 0 otherwise identifiability problem. distribution defined b equal 0 simultaneously.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Generalized logistic distribution — distrib","text":"Rathie, P. N. Swamee, P. K (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Generalized logistic distribution — distrib","text":"","code":"pgenlog(0.5) #> [1] 0.6133563 curve(dgenlog(x), xlim = c(-3,3)) rgenlog(100) #> [1] -1.5973060780 1.8469333593 -1.1589975196 0.7593395529 -0.8282933574 #> [6] -1.8189683961 0.8105236822 -0.5416872399 -1.2004121484 -0.9798103799 #> [11] -0.9785022696 1.5691416772 -0.8603533364 1.3809453014 0.9204901035 #> [16] 0.5239731763 -2.3422762392 0.7325788894 -0.5478148459 -0.0599068774 #> [21] 1.1901488702 1.1646961879 -0.0590565949 0.7806909828 -0.4647704850 #> [26] -0.0075752774 -0.9243616691 1.0953170642 1.8512968540 1.3970418212 #> [31] -0.2979343337 -0.7228095016 0.6628456534 1.2759491897 1.0976531692 #> [36] 1.2129034791 0.5578051124 0.6007138546 -0.2601975014 0.8920486902 #> [41] -1.3213149658 0.5028720514 -0.1446198286 0.1775284956 -1.1752357646 #> [46] -0.7879790879 0.7420850211 1.4842216037 -1.5792873026 0.3247211167 #> [51] 0.8404622028 0.9210866354 -1.4006066761 0.5707456255 0.8726364405 #> [56] -0.6964009210 -1.4182408425 -2.0657956884 1.1266853847 0.0647920253 #> [61] 0.7163431290 -0.0245650463 0.8370138896 0.9447948272 0.7990404726 #> [66] 0.9164235667 0.3023775397 -1.2655023258 1.1580985640 1.1146871184 #> [71] 0.3749429089 -2.0074099808 1.9613052633 0.4379508999 -0.5265390481 #> [76] 0.5595450199 0.8498043840 -0.9639220819 1.4336129057 -1.4662492396 #> [81] -0.2897429318 -0.8789979462 0.0008573161 0.1539664227 0.0910493076 #> [86] -0.5478950080 -0.4053581283 -0.7279654074 0.6357919217 -0.8118998328 #> [91] -0.5246200879 0.8736504377 -0.6128714069 1.0606306506 0.9817465941 #> [96] 0.9815781244 1.3703118892 -0.0453467373 -0.9027165981 -0.7590511416 qgenlog(0.95) #> [1] 1.5143"},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":null,"dir":"Reference","previous_headings":"","what":"The Generalized logistic distribution with skewness — distrib_sk","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"Density, distribution function, quantile function random generation generalized logistic distribution skewness.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"","code":"pgenlog_sk( q, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, skew = 0.5, lower.tail = TRUE ) dgenlog_sk(x, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, skew = 0.5) qgenlog_sk( k, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, skew = 0.5, lower.tail = TRUE ) rgenlog_sk(n, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, skew = 0.5)"},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Generalized logistic distribution with skewness — distrib_sk","text":", b, p parameters \\(\\le 0\\), restrictions.* mu mu parameter skew skewness parameter limited interval (-1, 1) lower.tail logical; TRUE (default), probabilities \\(P[X \\le x]\\) otherwise, \\(P[X > x]\\). x, q vector quantiles. k vector probabilities. n number observations. length(n) > 1, length taken number required","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"dgenlog_sk gives density, pgenlog_sk gives distribution function, qgenlog_sk gives quantile function, rgenlog_sk generates random deviates. length result determined n rgenlog_sk, maximum lengths numerical arguments functions.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"used distribution package given : $$f(x) = 2*((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(abs(x-mu)^p))))/ ((exp(-(x-mu)*(+ b* (abs(x-mu)^p)))+1)^2) * ((exp(-(skew*(x-mu))*(+b*(abs(skew*(x-mu))^p)))+1)^(-1)) $$ default values , b, p mu produces function mean 0 variance close 1. *Restrictions: p equals 0, b must 0 otherwise identifiability problem. distribution defined b equal 0 simultaneously.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"Rathie, P. N. Swamee, P. K (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Azzalini, . (1985) class distributions includes normal ones. Scandinavian Journal Statistics.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"","code":"pgenlog_sk(0.5) #> [1] 0.512874 curve(dgenlog_sk(x), xlim = c(-3,3)) rgenlog_sk(100) #> [1] 0.43630494 1.29794730 0.61424154 -1.20661352 -1.25415854 -0.54876535 #> [7] -1.46116518 0.83257457 1.06491590 0.46132542 0.73695417 -0.87584050 #> [13] -0.07586695 -1.25184153 1.88944177 0.83462772 -0.25806080 2.06705313 #> [19] 0.80927966 0.97483412 -0.50102668 1.11153506 -0.69216987 -0.72912284 #> [25] -0.21230722 1.34561158 0.45184039 -1.22681883 1.00820083 0.79471736 #> [31] 1.62184876 0.83017086 0.86769095 -0.46244725 1.40435763 0.38599960 #> [37] -0.64575217 0.41766584 0.77813887 1.59745002 1.13344840 0.17419203 #> [43] 0.01533669 -0.64057473 0.91545905 1.04898164 -1.38202626 0.63861411 #> [49] -1.05467774 0.89451685 -1.05825910 -1.55684372 -0.24236910 1.13016193 #> [55] -0.13850561 1.38241849 0.08245531 1.70420737 -1.41717486 -1.46252796 #> [61] -0.15525420 1.16040219 -0.62310335 0.33236226 0.81362536 0.18724094 #> [67] 1.13399050 1.02412511 -0.76918397 1.12161620 0.84858953 0.89537251 #> [73] -1.09670707 -0.33633274 0.31212661 1.60794555 0.64991920 -1.46867244 #> [79] 1.32086670 1.85665868 0.53129460 -0.77974607 1.39917444 -1.10554533 #> [85] 1.42319366 2.14963000 0.76137122 -1.21063921 2.03836809 1.02185140 #> [91] -0.92844392 -0.90145976 0.63249385 -0.18614750 -0.98244364 -1.25573682 #> [97] 1.32067127 0.69659250 2.17324976 0.74555478 qgenlog_sk(0.95) #> [1] 1.607905"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimization for a generalized logistic distribution — genlog_mle","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"Maximum likehood estimation parameters generalized logistic distribution.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"","code":"genlog_mle(parameters, data, hessian = F, alpha = 0.05)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"parameters Initial values parameters optimized following order c(, b, p, mu), mu can omitted set 0. data data utilized estimation. hessian logical value returns hessian, also returns parameters estimation's confidence interval. alpha Type error given calculate confidence intervals, used hessian = T.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"Return list components constrOptim \\(information, check function\\) extras: par best set parameters found. value value loglikelihood function corresponding par. counts two-element integer vector giving number calls likelihood function BFGS respectively. excludes calls needed compute Hessian, calls likelihood function compute finite-difference approximation gradient. convergence integer code. 0 indicates successful completion, 1 indicates iteration limit maxit reached. errors help(constrOptim). message character string giving additional information returned optimizer, NULL. outer.iterations gives number outer iterations (calls optim). barrier.value giving value barrier function optimum. hessian = T add: hessian symmetric matrix giving estimate (negative) Hessian solution found. Note Hessian unconstrained problem even box constraints active. bounds Return best parameters found upper lower bounds estimation.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"Maximum likehood estimation parameters distribution proposed package. function application constrOptim particular case needed distribution using method \"BFGS\". information output check help(constrOptim). used distribution package given : $$f(x) = ((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(|x-mu|^p)))) / ((exp(-(x-mu)*(+ b* (|x-mu|^p)))+1)^2)$$ help(dgenlog) parameters restrictions.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"Rathie, P. N. Swamee, P. K. (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Byrd, R. H., Lu, P., Nocedal, J. Zhu, C. (1995) limited memory algorithm bound constrained optimization. SIAM J. Scientific Computing, 16, 1190-1208.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"","code":"## Using generic parameter starting values datas <- rgenlog(10000, 1.5,2,2, 0) genlog_mle(c(.5,1.6, 1.5, 0),datas) #> $par #> [1] 1.394682441 2.054909656 1.798232661 0.004956888 #> #> $value #> [1] 8292.891 #> #> $counts #> function gradient #> 65 15 #> #> $convergence #> [1] 0 #> #> $message #> NULL #> #> $outer.iterations #> [1] 2 #> #> $barrier.value #> [1] 0.0002248638 #> ## Select parameters starting values with genlog_slider # \\donttest{ datas <- rgenlog(10000, 1.5,2,2, 0) if (manipulate::isAvailable()) { genlog_slider(datas, return_var = 'parameters') ## choose parameters } else { parameters <- c(1.345, 2, 2, -0.00510) } genlog_mle(parameters,datas) #> $par #> [1] 1.523082700 2.024397847 2.062656142 -0.001228966 #> #> $value #> [1] 8179.161 #> #> $counts #> function gradient #> 38 7 #> #> $convergence #> [1] 0 #> #> $message #> NULL #> #> $outer.iterations #> [1] 2 #> #> $barrier.value #> [1] 0.0002048217 #> # }"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"Maximum likehood estimation parameters generalized logistic distribution skewness.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"","code":"genlog_mle_sk(parameters, data, hessian = F, alpha = 0.05)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"parameters Initial values parameters optimized following order c(, b, p, mu, skew). data data utilized estimation. hessian logical value returns hessian, also returns parameters estimation's confidence interval. alpha Type error given calculate confidence intervals, used hessian = T.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"Return list components constrOptim \\(information, check function\\) extras: par best set parameters found. value value loglikelihood function corresponding par. counts two-element integer vector giving number calls likelihood function L-BFGS-B respectively. excludes calls needed compute Hessian, calls likelihood function compute finite-difference approximation gradient. convergence integer code. 0 indicates successful completion, 1 indicates iteration limit maxit reached. errors help(constrOptim). message character string giving additional information returned optimizer, NULL. outer.iterations gives number outer iterations (calls optim). barrier.value giving value barrier function optimum. hessian = T add: hessian symmetric matrix giving estimate (negative) Hessian solution found. Note Hessian unconstrained problem even box constraints active. bounds Return best parameters found upper lower bounds estimation.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"Maximum likehood estimation parameters distribution proposed package. function application constrOptim particular case needed distribution using method \"BFGS\". information output check help(constrOptim). used distribution package given : $$f(x) = 2*((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(abs(x-mu)^p))))/ ((exp(-(x-mu)*(+ b* (abs(x-mu)^p)))+1)^2) * ((exp(-(skew*(x-mu))*(+b*(abs(skew*(x-mu))^p)))+1)^(-1)) $$ help(dgenlog_sk) parameters restrictions.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"Rathie, P. N. Swamee, P. K. (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Azzalini, . (1985) class distributions includes normal ones. Scandinavian Journal Statistics. Byrd, R. H., Lu, P., Nocedal, J. Zhu, C. (1995) limited memory algorithm bound constrained optimization. SIAM J. Scientific Computing, 16, 1190-1208.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"","code":"## Using generic parameter starting values datas <- rgenlog_sk(10000, 0.3,0.9,1.5, 0, 0.9) genlog_mle_sk(c(0.3,0.9,1.5, 0, 0.9),datas) #> $par #> [1] 0.291935207 0.913139043 1.461818162 -0.006611291 0.917229274 #> #> $value #> [1] 10874.1 #> #> $counts #> function gradient #> 45 8 #> #> $convergence #> [1] 0 #> #> $message #> NULL #> #> $outer.iterations #> [1] 2 #> #> $barrier.value #> [1] 0.000151263 #> ## Select parameters starting values with genlog_slider # \\donttest{ datas <- rgenlog(10000, 1.5,2,2, 0) if (manipulate::isAvailable()) { genlog_slider(datas, return_var = 'parameters', skew = T) ## choose parameters } else { parameters <- c( 1, 1.5, 1.3, 0.5, -.4) } genlog_mle_sk(parameters, datas) #> $par #> [1] 1.573190648 1.870266416 2.149176935 0.004106287 -0.014384578 #> #> $value #> [1] 8334.158 #> #> $counts #> function gradient #> 84 20 #> #> $convergence #> [1] 0 #> #> $message #> NULL #> #> $outer.iterations #> [1] 2 #> #> $barrier.value #> [1] 0.0002064364 #> # }"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulating the Generalized logistic distribution — genlog_simu","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"Creating simulation generalized logistic distribution maximum likelihood estimation parameters parallelized processing code using foreach package.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"","code":"genlog_simu(real.par, init.par, sample.size = 100, k = 1000, seed = 555, threads = 1, progress.bar = T)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"real.par real parameters value distribution wich random sample taken. vector length 4, parameters values c(, b, p, mu) listed rgenlog, mu can omitted set 0. default values. init.par Initial values parameters optimized following order c(, b, p, mu). Can object returned genlog_slider. default values. sample.size sample size taken k simulation. k number simulations. seed seed given set.seed() function sampling process threads numbers CPU threads used parallel computing. threads number higher available maximum allowed used. progress.bar show progress bar thread simulations, default value TRUE.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"returns data.frame k rows (simulation) 7 columns following information: , b, p mu estimations using maximum likelihood estimation, info help(genlogis_mle) sample.size sample size used k simulation. convergence estimation's convergence status.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"used distribution package given : $$f(x) = ((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(|x-mu|^p)))) / ((exp(-(x-mu)*(+ b* (|x-mu|^p)))+1)^2)$$ distribution use help(dgenlog).","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"Rathie, P. N. Swamee, P. K. (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"","code":"genlog_simu(real.par = c(0.3, 0.9, 1.5, 0.0), init.par = c(0.9, 0.3, 0.2, 0.0), sample.size = 100, k = 50, threads = 2, seed = 200) #> Error in { if (progress.bar == T) { if (!exists(\"pb\")) pb <- tcltk::tkProgressBar(\"Parallel task\", min = 1, max = k) tcltk::setTkProgressBar(pb, i) } core()}: task 1 failed - \"[tcl] invalid command name \"toplevel\". #> \""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"Creating simulation generalized logistic distribution skewness maximum likelihood estimation parameters parallelized processing code using foreach package.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"","code":"genlog_simu_sk(real.par, init.par, sample.size = 100, k = 1000, seed = 555, threads = 1, progress.bar = T)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"real.par real parameters value distribution wich random sample taken. vector length 5, parameters values c(, b, p, mu) listed rgenlog, mu can omitted set 0. default values. init.par Initial values parameters optimized following order c(, b, p, mu, skew). Can object returned genlog_slider. default values. sample.size sample size taken k simulation. k number simulations. seed seed given set.seed() function sampling process threads numbers CPU threads used parallel computing. threads number higher available maximum allowed used. progress.bar show progress bar thread simulations, default value TRUE.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"returns data.frame k rows (simulation) 7 columns following information: , b, p mu estimations using maximum likelihood estimation, info help(genlogis_mle) sample.size sample size used k simulation. convergence estimation's convergence status.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"used distribution package given : $$f(x) = 2*((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(abs(x-mu)^p))))/ ((exp(-(x-mu)*(+ b* (abs(x-mu)^p)))+1)^2) * ((exp(-(skew*(x-mu))*(+b*(abs(skew*(x-mu))^p)))+1)^(-1)) $$","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"Rathie, P. N. Swamee, P. K (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Azzalini, . (1985) class distributions includes normal ones. Scandinavian Journal Statistics.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"","code":"genlog_simu_sk(real.par = c(0.3, 0.9, 1.5, 0.0, .9), init.par = c(0.9, 0.3, 0.2, 0.0, .9), sample.size = 100, k = 50, threads = 2, seed = 200) #> Error in { if (progress.bar == T) { if (!exists(\"pb\")) pb <- tcltk::tkProgressBar(\"Parallel task\", min = 1, max = k) tcltk::setTkProgressBar(pb, i) } core()}: task 1 failed - \"[tcl] invalid command name \"toplevel\". #> \""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":null,"dir":"Reference","previous_headings":"","what":"Slider for generalized logistic — genlog_slider","title":"Slider for generalized logistic — genlog_slider","text":"Make generalized logistic distribution slider compare histogram theoretical distribution","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Slider for generalized logistic — genlog_slider","text":"","code":"genlog_slider(data, return_var = NULL, mu_range = 10, skew = F)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Slider for generalized logistic — genlog_slider","text":"data vector data compare. return_var char string name parameters assigned mu_range number setup minimum maximum range value mu parameter skew logical, TRUE, model skewness used..","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Slider for generalized logistic — genlog_slider","text":"function plots interactive graphic RStudio Viewer panel. Also, parameters , b, p mu can returned return_var asked graphic.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Slider for generalized logistic — genlog_slider","text":"small gear top left graphic can slide parameters @param ,b,p,mu. used distribution package given : $$f(x) = ((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(|x-mu|^p)))) / ((exp(-(x-mu)*(+ b* (|x-mu|^p)))+1)^2)$$ density function printed defined parameters. skew = T model used used distribution given : $$f(x) = 2*((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(abs(x-mu)^p))))/ ((exp(-(x-mu)*(+ b* (abs(x-mu)^p)))+1)^2) * ((exp(-(skew*(x-mu))*(+b*(abs(skew*(x-mu))^p)))+1)^(-1)) $$#' information model (help(dgenlog_sk)) density function printed defined parameters. help(dgenlog) parameters restrictions. function requires RStudio run.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Slider for generalized logistic — genlog_slider","text":"Rathie, P. N. Swamee, P. K (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Azzalini, . (1985) class distributions includes normal ones. Scandinavian Journal Statistics.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Slider for generalized logistic — genlog_slider","text":"","code":"# \\donttest{ datas <- rgenlog(1000) if (manipulate::isAvailable()) { genlog_slider(datas, return_var = 'parameters') } # }"}] +[{"path":"https://pinduzera.github.io/genlogis/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Eduardo Hellas. Author, maintainer. Eduardo Monteiro. Author, contributor.","code":""},{"path":"https://pinduzera.github.io/genlogis/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Hellas E, Monteiro E (2023). genlogis: Generalized Logistic Distribution. R package version 1.0.1, https://pinduzera.github.io/genlogis/.","code":"@Manual{, title = {genlogis: Generalized Logistic Distribution}, author = {Eduardo Hellas and Eduardo Monteiro}, year = {2023}, note = {R package version 1.0.1}, url = {https://pinduzera.github.io/genlogis/}, }"},{"path":"https://pinduzera.github.io/genlogis/index.html","id":"r-genlogis","dir":"","previous_headings":"","what":"Generalized Logistic Distribution","title":"Generalized Logistic Distribution","text":"package provides basic distribution functions generalized logistic distribution proposed Rathie Swamee (2006) https://www.rroij.com/open-access/-new-generalized-logistic-distributions--applicationsbarreto-fhs-mota-jma--rathie-pn-.pdf. also interactive ‘RStudio’ plot better guessing dynamically initial values ease included optimization simulating. build academic work University Brasília (UnB) supervision Dr. Eduardo Monteiro.","code":""},{"path":"https://pinduzera.github.io/genlogis/index.html","id":"installing","dir":"","previous_headings":"","what":"Installing","title":"Generalized Logistic Distribution","text":"","code":"# To install the package install.packages(\"genlogis\") # If you want the version from GitHub: # install.packages(\"devtools\") devtools::install_github('pinduzera/genlogis')"},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":null,"dir":"Reference","previous_headings":"","what":"The Generalized logistic distribution — distrib","title":"The Generalized logistic distribution — distrib","text":"Density, distribution function, quantile function random generation generalized logistic distribution.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Generalized logistic distribution — distrib","text":"","code":"pgenlog(q, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, lower.tail = TRUE) dgenlog(x, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0) qgenlog(k, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, lower.tail = TRUE) rgenlog(n, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0)"},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Generalized logistic distribution — distrib","text":", b, p parameters \\(\\ge 0\\), restrictions.* mu mu parameter lower.tail logical; TRUE (default), probabilities \\(P[X \\le x]\\) otherwise, \\(P[X > x]\\). x, q vector quantiles. k vector probabilities. n number observations. length(n) > 1, length taken number required","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Generalized logistic distribution — distrib","text":"dgenlog gives density, pgenlog gives distribution function, qgenlog gives quantile function, rgenlog generates random deviates. length result determined n rgenlog, maximum lengths numerical arguments functions.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Generalized logistic distribution — distrib","text":"used distribution package given : $$f(x) = ((+ b*(1+p)*(|x-mu|^p))*exp(-(x-mu)*(+b*(|x-mu|^p)))) / ((exp(-(x-mu)*(+ b* (|x-mu|^p)))+1)^2)$$ default values , b, p mu produces function mean 0 variance close 1. *Restrictions: p equals 0, b must 0 otherwise identifiability problem. distribution defined b equal 0 simultaneously.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Generalized logistic distribution — distrib","text":"Rathie, P. N. Swamee, P. K (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Generalized logistic distribution — distrib","text":"","code":"pgenlog(0.5) #> [1] 0.6133563 curve(dgenlog(x), xlim = c(-3,3)) rgenlog(100) #> [1] -0.58487865 1.45535120 -1.46889615 -0.91101019 1.45349069 -0.87649678 #> [7] -0.29796606 0.99159044 1.61993039 1.66014645 -1.12590837 -1.46037202 #> [13] -0.13625403 -0.23910696 -1.28930842 -1.05411082 -0.51334350 -0.95719013 #> [19] 0.21603638 1.50837936 -0.12487832 0.72052045 1.47564446 -0.09202933 #> [25] 0.58947070 -1.16644053 0.75791848 -1.13376197 1.35714271 1.55459351 #> [31] 1.66513595 0.96534365 0.03704803 0.95015335 -1.63543670 0.83975884 #> [37] 0.03679816 -1.16524523 -1.27374907 -1.11013582 1.32224149 -0.09695106 #> [43] -1.41227725 -0.39517512 -0.20001973 1.19990649 -0.18538583 1.02946543 #> [49] 0.14773808 -0.21632478 -0.55154153 -1.50913041 0.21984348 -0.75968639 #> [55] 0.99350334 -1.24779420 1.23151962 -0.87781727 -0.52182090 -0.43726739 #> [61] -0.58780588 1.30634364 -0.73319389 1.58363459 1.80285648 -1.32977683 #> [67] -0.19253511 1.44026635 1.34154042 0.16363125 1.58408956 0.04192068 #> [73] -0.87434205 1.75934912 1.49073946 1.22830632 0.29385024 -1.00137134 #> [79] 0.13226600 -0.76136925 1.21006151 0.86227000 -1.27733886 -1.50443721 #> [85] -0.12078951 -0.33474386 -1.22875006 -0.97050601 0.11804355 0.26191866 #> [91] -0.64001558 -0.12807290 1.24580967 1.38185941 -0.69215165 1.27470863 #> [97] 0.78891284 -0.70083512 0.54013419 -1.04995675 qgenlog(0.95) #> [1] 1.5143"},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":null,"dir":"Reference","previous_headings":"","what":"The Generalized logistic distribution with skewness — distrib_sk","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"Density, distribution function, quantile function random generation generalized logistic distribution skewness.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"","code":"pgenlog_sk( q, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, skew = 0.5, lower.tail = TRUE ) dgenlog_sk(x, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, skew = 0.5) qgenlog_sk( k, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, skew = 0.5, lower.tail = TRUE ) rgenlog_sk(n, a = sqrt(2/pi), b = 0.5, p = 2, mu = 0, skew = 0.5)"},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Generalized logistic distribution with skewness — distrib_sk","text":", b, p parameters \\(\\le 0\\), restrictions.* mu mu parameter skew skewness parameter limited interval (-1, 1) lower.tail logical; TRUE (default), probabilities \\(P[X \\le x]\\) otherwise, \\(P[X > x]\\). x, q vector quantiles. k vector probabilities. n number observations. length(n) > 1, length taken number required","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"dgenlog_sk gives density, pgenlog_sk gives distribution function, qgenlog_sk gives quantile function, rgenlog_sk generates random deviates. length result determined n rgenlog_sk, maximum lengths numerical arguments functions.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"used distribution package given : $$f(x) = 2*((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(abs(x-mu)^p))))/ ((exp(-(x-mu)*(+ b* (abs(x-mu)^p)))+1)^2) * ((exp(-(skew*(x-mu))*(+b*(abs(skew*(x-mu))^p)))+1)^(-1)) $$ default values , b, p mu produces function mean 0 variance close 1. *Restrictions: p equals 0, b must 0 otherwise identifiability problem. distribution defined b equal 0 simultaneously.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"Rathie, P. N. Swamee, P. K (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Azzalini, . (1985) class distributions includes normal ones. Scandinavian Journal Statistics.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/distrib_sk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Generalized logistic distribution with skewness — distrib_sk","text":"","code":"pgenlog_sk(0.5) #> [1] 0.512874 curve(dgenlog_sk(x), xlim = c(-3,3)) rgenlog_sk(100) #> [1] -0.77276500 1.37097793 -0.02268508 0.36851625 1.57153600 1.45826463 #> [7] -0.55655663 0.18104925 1.38609156 -0.21696826 0.35434232 -1.59145325 #> [13] -0.18135508 0.23780370 -0.24521378 -0.80661894 -0.64171041 1.22743907 #> [19] 1.76726108 -0.60244154 0.10401794 -0.60142224 0.89111620 1.59353793 #> [25] 1.01015221 1.01740622 1.66968588 1.14986251 2.03631266 -0.64071028 #> [31] -1.22546490 -1.06311424 0.94468372 1.23886586 -1.18022493 0.94200612 #> [37] -0.94759266 -0.15641882 1.17088219 -0.74178133 1.32119142 2.30336254 #> [43] 0.42060173 1.18189933 1.07120702 -1.81927403 -1.31301868 1.51086021 #> [49] 1.04843359 1.53032193 0.82217480 -0.38277004 1.07544115 -0.49334181 #> [55] -0.81883176 0.34271773 0.79674170 0.66422579 1.06096462 0.71374336 #> [61] 1.71960957 -1.31310191 -0.47382404 1.13969676 1.07412219 0.88818108 #> [67] 1.09718896 -0.13889023 0.33079437 0.70049110 1.13088202 -1.56048760 #> [73] -0.69649877 -1.30087661 0.19790769 0.88672828 0.79716549 -1.34966557 #> [79] -0.07854816 1.06702341 -1.43640468 0.06418204 1.00582321 -0.47469418 #> [85] 0.71631664 -1.40898063 -0.32527271 1.09208590 1.58187659 0.09131728 #> [91] -1.28352662 1.33790357 0.93621750 0.09701173 0.55695498 -1.09307987 #> [97] -0.44667644 1.58183228 1.59453987 1.65569265 qgenlog_sk(0.95) #> [1] 1.607905"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimization for a generalized logistic distribution — genlog_mle","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"Maximum likehood estimation parameters generalized logistic distribution.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"","code":"genlog_mle(parameters, data, hessian = F, alpha = 0.05)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"parameters Initial values parameters optimized following order c(, b, p, mu), mu can omitted set 0. data data utilized estimation. hessian logical value returns hessian, also returns parameters estimation's confidence interval. alpha Type error given calculate confidence intervals, used hessian = T.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"Return list components constrOptim \\(information, check function\\) extras: par best set parameters found. value value loglikelihood function corresponding par. counts two-element integer vector giving number calls likelihood function BFGS respectively. excludes calls needed compute Hessian, calls likelihood function compute finite-difference approximation gradient. convergence integer code. 0 indicates successful completion, 1 indicates iteration limit maxit reached. errors help(constrOptim). message character string giving additional information returned optimizer, NULL. outer.iterations gives number outer iterations (calls optim). barrier.value giving value barrier function optimum. hessian = T add: hessian symmetric matrix giving estimate (negative) Hessian solution found. Note Hessian unconstrained problem even box constraints active. bounds Return best parameters found upper lower bounds estimation.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"Maximum likehood estimation parameters distribution proposed package. function application constrOptim particular case needed distribution using method \"BFGS\". information output check help(constrOptim). used distribution package given : $$f(x) = ((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(|x-mu|^p)))) / ((exp(-(x-mu)*(+ b* (|x-mu|^p)))+1)^2)$$ help(dgenlog) parameters restrictions.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"Rathie, P. N. Swamee, P. K. (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Byrd, R. H., Lu, P., Nocedal, J. Zhu, C. (1995) limited memory algorithm bound constrained optimization. SIAM J. Scientific Computing, 16, 1190-1208.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Optimization for a generalized logistic distribution — genlog_mle","text":"","code":"## Using generic parameter starting values datas <- rgenlog(10000, 1.5,2,2, 0) genlog_mle(c(.5,1.6, 1.5, 0),datas) #> $par #> [1] 1.473250136 2.044438004 1.907402254 -0.007229233 #> #> $value #> [1] 8213.259 #> #> $counts #> function gradient #> 65 16 #> #> $convergence #> [1] 0 #> #> $message #> NULL #> #> $outer.iterations #> [1] 2 #> #> $barrier.value #> [1] 0.0002160534 #> ## Select parameters starting values with genlog_slider # \\donttest{ datas <- rgenlog(10000, 1.5,2,2, 0) if (manipulate::isAvailable()) { genlog_slider(datas, return_var = 'parameters') ## choose parameters } else { parameters <- c(1.345, 2, 2, -0.00510) } genlog_mle(parameters,datas) #> $par #> [1] 1.462043387 2.047639808 1.985611585 -0.004823423 #> #> $value #> [1] 8214.426 #> #> $counts #> function gradient #> 39 7 #> #> $convergence #> [1] 0 #> #> $message #> NULL #> #> $outer.iterations #> [1] 2 #> #> $barrier.value #> [1] 0.0002110456 #> # }"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"Maximum likehood estimation parameters generalized logistic distribution skewness.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"","code":"genlog_mle_sk(parameters, data, hessian = F, alpha = 0.05)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"parameters Initial values parameters optimized following order c(, b, p, mu, skew). data data utilized estimation. hessian logical value returns hessian, also returns parameters estimation's confidence interval. alpha Type error given calculate confidence intervals, used hessian = T.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"Return list components constrOptim \\(information, check function\\) extras: par best set parameters found. value value loglikelihood function corresponding par. counts two-element integer vector giving number calls likelihood function L-BFGS-B respectively. excludes calls needed compute Hessian, calls likelihood function compute finite-difference approximation gradient. convergence integer code. 0 indicates successful completion, 1 indicates iteration limit maxit reached. errors help(constrOptim). message character string giving additional information returned optimizer, NULL. outer.iterations gives number outer iterations (calls optim). barrier.value giving value barrier function optimum. hessian = T add: hessian symmetric matrix giving estimate (negative) Hessian solution found. Note Hessian unconstrained problem even box constraints active. bounds Return best parameters found upper lower bounds estimation.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"Maximum likehood estimation parameters distribution proposed package. function application constrOptim particular case needed distribution using method \"BFGS\". information output check help(constrOptim). used distribution package given : $$f(x) = 2*((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(abs(x-mu)^p))))/ ((exp(-(x-mu)*(+ b* (abs(x-mu)^p)))+1)^2) * ((exp(-(skew*(x-mu))*(+b*(abs(skew*(x-mu))^p)))+1)^(-1)) $$ help(dgenlog_sk) parameters restrictions.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"Rathie, P. N. Swamee, P. K. (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Azzalini, . (1985) class distributions includes normal ones. Scandinavian Journal Statistics. Byrd, R. H., Lu, P., Nocedal, J. Zhu, C. (1995) limited memory algorithm bound constrained optimization. SIAM J. Scientific Computing, 16, 1190-1208.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_mle_sk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Optimization for a generalized logistic distribution with skewness — genlog_mle_sk","text":"","code":"## Using generic parameter starting values datas <- rgenlog_sk(10000, 0.3,0.9,1.5, 0, 0.9) genlog_mle_sk(c(0.3,0.9,1.5, 0, 0.9),datas) #> $par #> [1] 0.298475326 0.904429619 1.482780468 -0.002193411 0.899402074 #> #> $value #> [1] 10936.98 #> #> $counts #> function gradient #> 48 7 #> #> $convergence #> [1] 0 #> #> $message #> NULL #> #> $outer.iterations #> [1] 2 #> #> $barrier.value #> [1] 0.0001565813 #> ## Select parameters starting values with genlog_slider # \\donttest{ datas <- rgenlog(10000, 1.5,2,2, 0) if (manipulate::isAvailable()) { genlog_slider(datas, return_var = 'parameters', skew = T) ## choose parameters } else { parameters <- c( 1, 1.5, 1.3, 0.5, -.4) } genlog_mle_sk(parameters, datas) #> $par #> [1] 1.5176416737 2.0333834227 2.0178149481 -0.0003373249 0.0038627704 #> #> $value #> [1] 8176.155 #> #> $counts #> function gradient #> 86 18 #> #> $convergence #> [1] 0 #> #> $message #> NULL #> #> $outer.iterations #> [1] 2 #> #> $barrier.value #> [1] 0.0002076098 #> # }"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulating the Generalized logistic distribution — genlog_simu","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"Creating simulation generalized logistic distribution maximum likelihood estimation parameters parallelized processing code using foreach package.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"","code":"genlog_simu(real.par, init.par, sample.size = 100, k = 1000, seed = 555, threads = 1, progress.bar = T)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"real.par real parameters value distribution wich random sample taken. vector length 4, parameters values c(, b, p, mu) listed rgenlog, mu can omitted set 0. default values. init.par Initial values parameters optimized following order c(, b, p, mu). Can object returned genlog_slider. default values. sample.size sample size taken k simulation. k number simulations. seed seed given set.seed() function sampling process threads numbers CPU threads used parallel computing. threads number higher available maximum allowed used. progress.bar show progress bar thread simulations, default value TRUE.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"returns data.frame k rows (simulation) 7 columns following information: , b, p mu estimations using maximum likelihood estimation, info help(genlogis_mle) sample.size sample size used k simulation. convergence estimation's convergence status.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"used distribution package given : $$f(x) = ((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(|x-mu|^p)))) / ((exp(-(x-mu)*(+ b* (|x-mu|^p)))+1)^2)$$ distribution use help(dgenlog).","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"Rathie, P. N. Swamee, P. K. (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulating the Generalized logistic distribution — genlog_simu","text":"","code":"genlog_simu(real.par = c(0.3, 0.9, 1.5, 0.0), init.par = c(0.9, 0.3, 0.2, 0.0), sample.size = 100, k = 50, threads = 2, seed = 200) #> Error in { if (progress.bar == T) { if (!exists(\"pb\")) pb <- tcltk::tkProgressBar(\"Parallel task\", min = 1, max = k) tcltk::setTkProgressBar(pb, i) } core()}: task 1 failed - \"[tcl] invalid command name \"toplevel\". #> \""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"Creating simulation generalized logistic distribution skewness maximum likelihood estimation parameters parallelized processing code using foreach package.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"","code":"genlog_simu_sk(real.par, init.par, sample.size = 100, k = 1000, seed = 555, threads = 1, progress.bar = T)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"real.par real parameters value distribution wich random sample taken. vector length 5, parameters values c(, b, p, mu) listed rgenlog, mu can omitted set 0. default values. init.par Initial values parameters optimized following order c(, b, p, mu, skew). Can object returned genlog_slider. default values. sample.size sample size taken k simulation. k number simulations. seed seed given set.seed() function sampling process threads numbers CPU threads used parallel computing. threads number higher available maximum allowed used. progress.bar show progress bar thread simulations, default value TRUE.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"returns data.frame k rows (simulation) 7 columns following information: , b, p mu estimations using maximum likelihood estimation, info help(genlogis_mle) sample.size sample size used k simulation. convergence estimation's convergence status.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"used distribution package given : $$f(x) = 2*((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(abs(x-mu)^p))))/ ((exp(-(x-mu)*(+ b* (abs(x-mu)^p)))+1)^2) * ((exp(-(skew*(x-mu))*(+b*(abs(skew*(x-mu))^p)))+1)^(-1)) $$","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"Rathie, P. N. Swamee, P. K (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Azzalini, . (1985) class distributions includes normal ones. Scandinavian Journal Statistics.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_simu_sk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulating the Generalized logistic distribution with skewness — genlog_simu_sk","text":"","code":"genlog_simu_sk(real.par = c(0.3, 0.9, 1.5, 0.0, .9), init.par = c(0.9, 0.3, 0.2, 0.0, .9), sample.size = 100, k = 50, threads = 2, seed = 200) #> Error in { if (progress.bar == T) { if (!exists(\"pb\")) pb <- tcltk::tkProgressBar(\"Parallel task\", min = 1, max = k) tcltk::setTkProgressBar(pb, i) } core()}: task 1 failed - \"[tcl] invalid command name \"toplevel\". #> \""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":null,"dir":"Reference","previous_headings":"","what":"Slider for generalized logistic — genlog_slider","title":"Slider for generalized logistic — genlog_slider","text":"Make generalized logistic distribution slider compare histogram theoretical distribution","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Slider for generalized logistic — genlog_slider","text":"","code":"genlog_slider(data, return_var = NULL, mu_range = 10, skew = F)"},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Slider for generalized logistic — genlog_slider","text":"data vector data compare. return_var char string name parameters assigned mu_range number setup minimum maximum range value mu parameter skew logical, TRUE, model skewness used..","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Slider for generalized logistic — genlog_slider","text":"function plots interactive graphic RStudio Viewer panel. Also, parameters , b, p mu can returned return_var asked graphic.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Slider for generalized logistic — genlog_slider","text":"small gear top left graphic can slide parameters @param ,b,p,mu. used distribution package given : $$f(x) = ((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(|x-mu|^p)))) / ((exp(-(x-mu)*(+ b* (|x-mu|^p)))+1)^2)$$ density function printed defined parameters. skew = T model used used distribution given : $$f(x) = 2*((+ b*(1+p)*(abs(x-mu)^p))*exp(-(x-mu)*(+b*(abs(x-mu)^p))))/ ((exp(-(x-mu)*(+ b* (abs(x-mu)^p)))+1)^2) * ((exp(-(skew*(x-mu))*(+b*(abs(skew*(x-mu))^p)))+1)^(-1)) $$#' information model (help(dgenlog_sk)) density function printed defined parameters. help(dgenlog) parameters restrictions. function requires RStudio run.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Slider for generalized logistic — genlog_slider","text":"Rathie, P. N. Swamee, P. K (2006) new invertible generalized logistic distribution approximation normal distribution, Technical Research Report Statistics, 07/2006, Dept. Statistics, Univ. Brasilia, Brasilia, Brazil. Azzalini, . (1985) class distributions includes normal ones. Scandinavian Journal Statistics.","code":""},{"path":"https://pinduzera.github.io/genlogis/reference/genlog_slider.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Slider for generalized logistic — genlog_slider","text":"","code":"# \\donttest{ datas <- rgenlog(1000) if (manipulate::isAvailable()) { genlog_slider(datas, return_var = 'parameters') } # }"}]