Updated slides.

Updated README. Added ... parameter for shiny functions to pass parameters to runApp.
jbryer · Nov 14, 2023 · cf39786 · cf39786
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diff --git a/R/psa_shiny.R b/R/psa_shiny.R
@@ -1,13 +1,19 @@
 #' Run the PSA shiny application.
 #' 
+#' @param ... other parameters passed to [shiny::runApp].
 #' @export
-psa_shiny <- function() {
-	shiny::runApp(paste0(find.package(package='psa'), '/shiny/psa'))
+psa_shiny <- function(...) {
+	shiny::runApp(
+		appDir = paste0(find.package(package='psa'), '/shiny/psa'),
+		...)
 }
 
 #' Run the PSA simulation shiny application.
 #' 
+#' @param ... other parameters passed to [shiny::runApp].
 #' @export
-psa_simulation_shiny <- function() {
-	shiny::runApp(paste0(find.package(package='psa'), '/shiny/psa_simulation'))
+psa_simulation_shiny <- function(...) {
+	shiny::runApp(
+		appDir = paste0(find.package(package='psa'), '/shiny/psa_simulation'),
+		...)
 }
diff --git a/README.Rmd b/README.Rmd
@@ -44,7 +44,7 @@ ggplot(psa_citations, aes(x = Year, y = Citations, color = Search_Term)) +
 
 ## Slides
 
-[Click here](Slides/Intro_PSA.pdf) for latest version slides introducing propensity score analysis.
+The latest version slides introducing propensity score analysis: [PDF](Slides/Intro_PSA.pdf) or [HTML](Slides/Intro_PSA.html).
 
 <!--
 ## CUNY MSDS Talk
@@ -176,42 +176,6 @@ There are a number of R packages available for conducting propensity score analy
 * [`rpart`](http://cran.r-project.org/web/packages/rpart/index.html) (Therneau, Atkinson, & Ripley, 2012) Recursive Partitioning
 * [`TriMatch`](http://jason.bryer.org/TriMatch) (Bryer, 2013) Propensity Score Matching for Non-Binary Treatments
 
-## References
-
-Rosenbaum, P.R., & Rubin, D.B. (1983). [The central role of the propensity score in observational studies for causal effects](http://faculty.smu.edu/Millimet/classes/eco7377/papers/rosenbaum%20rubin%2083a.pdf). *Biometrika, 70*(1), 41-55.
-
-Rosenbaum, P.R. (2010). *Design of Observational Studies*. New York: Springer.
-
-Austin, P. C. (2011). Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples. *Statistics in Medicine, 30*.
-
-Bryer, J. (2011). multilevelPSA: Multilevel propensity score analysis [Computer software manual]. Retrieved from http://github.com/jbryer/multilevelPSA 
-
-Bryer, J., & Pruzek, R.M. (2011). An international comparison of private and public schools using multilevel propensity score methods and graphics (Abstract). *Multivariate Behavioral Research, 46*(6), 1010-1011.
-
-Helmreich, J. E., & Pruzek, R. M. (2009). PSAgraphics: An R package to support propensity score analysis. *Journal of Statistical Software, 29*(6). Available from http://www.jstatsoft.org/v29/i06/paper
-
-Ho, D.E., Imai, K., King, G., and Stuart, E.A (2011). [MatchIt: Nonparametric Preprocessing for Parametric Causal Inference](http://www.jstatsoft.org/v42/i08/). *Journal of Statistical Software 42*(8).
-
-Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased Recursive Partitioning: A Conditional Inference Framework. *Journal of Computational and Graphical Statistics, 15*(3), 651--674.
-
-R Core Team (2012). [R: A language and environment for statistical computing](http://www.R-project.org/). R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0.
-
-Rosenbaum, P.R. (2005). Sensitivity analysis in observational studies. In B.S. Everitt & D.C. Howell *Encyclopedia of Statistics in Behavioral Science*, pp. 1809-1814. Chichester: John Wiley & Sons.
-
-Rosenbaum, P.R. (2012). Testing one hypothesis twice in observational studies. *Biometrika*.
-
-Sekhon, J.S. (2011). [Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R](http://www.jstatsoft.org/v42/i07/). *Journal of Statistical Software, 42*(7), 1-52.
-
-Shadish, W.R., Clark, M.H., & Steiner, P.M. (2008). Can nonrandomized experiments yield accurate answers? A randomized experiment comparing random and nonrandom assignments. *Journal of the American Statistical Association, 103*(484). 1334-1356.
-
-Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. *Statistical Science, 25*, 1-21.
-
-Stuart, E.A., & Rubin, D.B. (2007). Best practices in quasi-experimental designs: Matching methods for causal inference. Chapter 11 (pp. 155-176) in J. Osborne (Ed.). *Best Practices in Quantitative Social Science*. Thousand Oaks, CA: Sage Publications.
-
-Therneau, T., Atkinson, B., & Ripley, B. (2012). [rpart: Recursive Partitioning](http://CRAN.R-project.org/package=rpart). R package version 4.0-1. 
-
-Thoemmes, F. J., & Kim, E. S. (2011). A systematic review of propensity score methods in the social sciences. Multivariate Behavioral Research, 46, 90-118.
-
 
 ## Code of Conduct
 

diff --git a/README.md b/README.md
@@ -42,8 +42,8 @@ be demonstrated.
 
 ## Slides
 
-[Click here](Slides/Intro_PSA.pdf) for latest version slides introducing
-propensity score analysis.
+The latest version slides introducing propensity score analysis:
+[PDF](Slides/Intro_PSA.pdf) or [HTML](Slides/Intro_PSA.html).
 
 <!--
 ## CUNY MSDS Talk
@@ -219,79 +219,6 @@ score analysis. These are the packages this workshop will make use of:
 - [`TriMatch`](http://jason.bryer.org/TriMatch) (Bryer, 2013) Propensity
   Score Matching for Non-Binary Treatments
 
-## References
-
-Rosenbaum, P.R., & Rubin, D.B. (1983). [The central role of the
-propensity score in observational studies for causal
-effects](http://faculty.smu.edu/Millimet/classes/eco7377/papers/rosenbaum%20rubin%2083a.pdf).
-*Biometrika, 70*(1), 41-55.
-
-Rosenbaum, P.R. (2010). *Design of Observational Studies*. New York:
-Springer.
-
-Austin, P. C. (2011). Comparing paired vs non-paired statistical methods
-of analyses when making inferences about absolute risk reductions in
-propensity-score matched samples. *Statistics in Medicine, 30*.
-
-Bryer, J. (2011). multilevelPSA: Multilevel propensity score analysis
-\[Computer software manual\]. Retrieved from
-<http://github.com/jbryer/multilevelPSA>
-
-Bryer, J., & Pruzek, R.M. (2011). An international comparison of private
-and public schools using multilevel propensity score methods and
-graphics (Abstract). *Multivariate Behavioral Research, 46*(6),
-1010-1011.
-
-Helmreich, J. E., & Pruzek, R. M. (2009). PSAgraphics: An R package to
-support propensity score analysis. *Journal of Statistical Software,
-29*(6). Available from <http://www.jstatsoft.org/v29/i06/paper>
-
-Ho, D.E., Imai, K., King, G., and Stuart, E.A (2011). [MatchIt:
-Nonparametric Preprocessing for Parametric Causal
-Inference](http://www.jstatsoft.org/v42/i08/). *Journal of Statistical
-Software 42*(8).
-
-Hothorn, T., Hornik, K., & Zeileis, A. (2006). Unbiased Recursive
-Partitioning: A Conditional Inference Framework. *Journal of
-Computational and Graphical Statistics, 15*(3), 651–674.
-
-R Core Team (2012). [R: A language and environment for statistical
-computing](http://www.R-project.org/). R Foundation for Statistical
-Computing, Vienna, Austria. ISBN 3-900051-07-0.
-
-Rosenbaum, P.R. (2005). Sensitivity analysis in observational studies.
-In B.S. Everitt & D.C. Howell *Encyclopedia of Statistics in Behavioral
-Science*, pp. 1809-1814. Chichester: John Wiley & Sons.
-
-Rosenbaum, P.R. (2012). Testing one hypothesis twice in observational
-studies. *Biometrika*.
-
-Sekhon, J.S. (2011). [Multivariate and Propensity Score Matching
-Software with Automated Balance Optimization: The Matching Package for
-R](http://www.jstatsoft.org/v42/i07/). *Journal of Statistical Software,
-42*(7), 1-52.
-
-Shadish, W.R., Clark, M.H., & Steiner, P.M. (2008). Can nonrandomized
-experiments yield accurate answers? A randomized experiment comparing
-random and nonrandom assignments. *Journal of the American Statistical
-Association, 103*(484). 1334-1356.
-
-Stuart, E. A. (2010). Matching methods for causal inference: A review
-and a look forward. *Statistical Science, 25*, 1-21.
-
-Stuart, E.A., & Rubin, D.B. (2007). Best practices in quasi-experimental
-designs: Matching methods for causal inference. Chapter 11 (pp. 155-176)
-in J. Osborne (Ed.). *Best Practices in Quantitative Social Science*.
-Thousand Oaks, CA: Sage Publications.
-
-Therneau, T., Atkinson, B., & Ripley, B. (2012). [rpart: Recursive
-Partitioning](http://CRAN.R-project.org/package=rpart). R package
-version 4.0-1.
-
-Thoemmes, F. J., & Kim, E. S. (2011). A systematic review of propensity
-score methods in the social sciences. Multivariate Behavioral Research,
-46, 90-118.
-
 ## Code of Conduct
 
 Please note that the psa project is released with a [Contributor Code of

diff --git a/Slides/Intro_PSA.Rmd b/Slides/Intro_PSA.Rmd
@@ -1,6 +1,6 @@
 ---
-title: "Introduction to Propensity Score Analysis with R"
-# subtitle: "Propensity Score Analysis with R"
+title: "A Visual Introduction to Propensity Score Analysis with R"
+subtitle: "New York Open Statistical Programming Meetup"
 author: "Jason Bryer, Ph.D."
 # date: "`r format(Sys.Date(), '%B %d, %Y')`"
 output:

diff --git a/Slides/Intro_PSA.html b/Slides/Intro_PSA.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <html lang="" xml:lang="">
   <head>
-    <title>Introduction to Propensity Score Analysis with R</title>
+    <title>A Visual Introduction to Propensity Score Analysis with R</title>
     <meta charset="utf-8" />
     <meta name="author" content="Jason Bryer, Ph.D." />
     <script src="libs/header-attrs/header-attrs.js"></script>
@@ -20,10 +20,10 @@
 
 
 
-# Introduction to Propensity Score Analysis with R
-## 
+# A Visual Introduction to Propensity Score Analysis with R
+## New York Open Statistical Programming Meetup
 ### Jason Bryer, Ph.D.
-### Last updated: November 13, 2023
+### Last updated: November 14, 2023
 
 
 ---
@@ -1256,56 +1256,56 @@
 ## Stratification Results:
 ##    Complete estimate = 1587
 ##    Complete CI = [224, 2950]
-##    Bootstrap pooled estimate = 1624
-##    Bootstrap weighted pooled estimate = 1625
-##    Bootstrap pooled CI = [196, 3053]
+##    Bootstrap pooled estimate = 1610
+##    Bootstrap weighted pooled estimate = 1558
+##    Bootstrap pooled CI = [234, 2985]
 ##    68% of bootstrap samples have confidence intervals that do not span zero.
 ##       68% positive.
 ##       0% negative.
 ## ctree Results:
 ##    Complete estimate = 1598
 ##    Complete CI = [-6.62, 3203]
-##    Bootstrap pooled estimate = 1592
-##    Bootstrap weighted pooled estimate = 1615
-##    Bootstrap pooled CI = [325, 2858]
-##    38% of bootstrap samples have confidence intervals that do not span zero.
-##       38% positive.
+##    Bootstrap pooled estimate = 1654
+##    Bootstrap weighted pooled estimate = 1643
+##    Bootstrap pooled CI = [228, 3080]
+##    37.1% of bootstrap samples have confidence intervals that do not span zero.
+##       37.1% positive.
 ##       0% negative.
 ## rpart Results:
 ##    Complete estimate = 1332
 ##    Complete CI = [-295, 2959]
-##    Bootstrap pooled estimate = 1434
-##    Bootstrap weighted pooled estimate = 1419
-##    Bootstrap pooled CI = [-286, 3154]
+##    Bootstrap pooled estimate = 1529
+##    Bootstrap weighted pooled estimate = 1537
+##    Bootstrap pooled CI = [24.7, 3034]
 ##    31% of bootstrap samples have confidence intervals that do not span zero.
 ##       31% positive.
 ##       0% negative.
 ## Matching Results:
 ##    Complete estimate = 1827
 ##    Complete CI = [1210, 2444]
-##    Bootstrap pooled estimate = 1537
-##    Bootstrap weighted pooled estimate = 1540
-##    Bootstrap pooled CI = [-332, 3406]
-##    87% of bootstrap samples have confidence intervals that do not span zero.
-##       86% positive.
-##       1% negative.
+##    Bootstrap pooled estimate = 1494
+##    Bootstrap weighted pooled estimate = 1463
+##    Bootstrap pooled CI = [-297, 3285]
+##    84% of bootstrap samples have confidence intervals that do not span zero.
+##       84% positive.
+##       0% negative.
 ## MatchIt Results:
 ##    Complete estimate = 2031
 ##    Complete CI = [797, 3266]
-##    Bootstrap pooled estimate = 1855
-##    Bootstrap weighted pooled estimate = 1802
-##    Bootstrap pooled CI = [373, 3336]
-##    73% of bootstrap samples have confidence intervals that do not span zero.
-##       73% positive.
+##    Bootstrap pooled estimate = 1899
+##    Bootstrap weighted pooled estimate = 1907
+##    Bootstrap pooled CI = [410, 3389]
+##    78% of bootstrap samples have confidence intervals that do not span zero.
+##       78% positive.
 ##       0% negative.
 ## Weighting Results:
 ##    Complete estimate = 1641
 ##    Complete CI = [380, 2902]
-##    Bootstrap pooled estimate = 1639
-##    Bootstrap weighted pooled estimate = 1627
-##    Bootstrap pooled CI = [176, 3102]
-##    73% of bootstrap samples have confidence intervals that do not span zero.
-##       73% positive.
+##    Bootstrap pooled estimate = 1622
+##    Bootstrap weighted pooled estimate = 1602
+##    Bootstrap pooled CI = [227, 3016]
+##    75% of bootstrap samples have confidence intervals that do not span zero.
+##       75% positive.
 ##       0% negative.
 ```
 

diff --git a/Slides/Intro_PSA.pdf b/Slides/Intro_PSA.pdf
diff --git a/Slides/Intro_PSA_files/figure-html/unnamed-chunk-60-1.png b/Slides/Intro_PSA_files/figure-html/unnamed-chunk-60-1.png
diff --git a/Slides/Intro_PSA_files/figure-html/unnamed-chunk-61-1.png b/Slides/Intro_PSA_files/figure-html/unnamed-chunk-61-1.png
diff --git a/Slides/Intro_PSA_files/figure-html/unnamed-chunk-62-1.png b/Slides/Intro_PSA_files/figure-html/unnamed-chunk-62-1.png
diff --git a/Slides/Intro_PSA_files/figure-html/unnamed-chunk-63-1.png b/Slides/Intro_PSA_files/figure-html/unnamed-chunk-63-1.png
diff --git a/man/figures/README-loess_plot-1.png b/man/figures/README-loess_plot-1.png
diff --git a/man/figures/README-matching_plot-1.png b/man/figures/README-matching_plot-1.png
diff --git a/man/psa_shiny.Rd b/man/psa_shiny.Rd
diff --git a/man/psa_simulation_shiny.Rd b/man/psa_simulation_shiny.Rd