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Reproduction Instructions

Note: In order to reproduce this analysis, you need the R package separation from GitHub, which you can install using devtools::install_github("carlislerainey/separation"). See below for the details about other R pacakges.

Makefile

To reproduce the analysis, first clear all created files. Run:

make cleanALL

Then reproduce the analysis. Run:

make

makeing

The Makfile describes the structure of the code and allows the user to reproduce the entire analysis or portions of it.

  • make all or just make reproduces the entire analysis, including the simulations, which take about five hours.
  • make dag reproduces a DAG that shows the structure of the dependencies in the Makefile.
  • make sims reproduces the .rds files of the simulations (created by both simulations.R and sample-size-simulations.R) and saves them in the simulations directory. You can monitor the progress of simulations.R and sample-size-simulations.R with simulations.log and sample-size-simulations.log, respectively. This takes about five hours.
  • make simplots reproduces the figures summarizing the simulations (Figures 2-5 and 8 from the main paper as well as figures for the appendix) and saves them in the manuscript/figs directory.
  • make ge reproduces our re-analysis of George and Epstein (1992) and saves the figures to the manuscript/figs directory.
  • make weisiger reproduces our re-analysis of Weisiger (2014) and saves the figures to the manuscript/figs directory.
  • make manuscript recompiles the LaTeX manuscript small.tex into small.pdf and small-appendix.tex into small-appendix.pdf. It automatically handles the bibliography.
  • make readme knits this document from the .Rmd file.
  • make computedvalues knits computed-values.pdf, which creates tables of the numeric quantities reported in the text.

Cleaning

You can clear any produced files from by the code by running make clean* and using any of the phonies above. For example,make cleandag removes the figure makefile-dag.png. To clean the entire directory, run make cleanALL (I put ALL in caps to remind myself of the consequences).

illustrate-bias-annotated.pdf

We used a combination of ggplot and Apple Keynote to create Figure 1 manuscript/figs/illustrate-bias-annotated.pdf, which illustrates the source of the small sample bias. The R script R/illustrate-bias.R create the underlying plot manuscript/figs/illustrate-bias.pdf, but we added the annotations manually in Keynote.

Additional Notes

  • The simulations take about five hours. We’ve set them up to run in parallel on four clusters. You might speed this up with a change here.
  • The code automatically stores the packages used in the last run in the file session-info.txt.
  • There is no log file, but all the figures are created and saved in the manuscript/figs directory. All quantities reported in the manuscript are computed and/or reported in the file computed-values.pdf.

R

We ran the analysis using the system below.

R.version
##                _                           
## platform       x86_64-apple-darwin15.6.0   
## arch           x86_64                      
## os             darwin15.6.0                
## system         x86_64, darwin15.6.0        
## status                                     
## major          3                           
## minor          6.1                         
## year           2019                        
## month          07                          
## day            05                          
## svn rev        76782                       
## language       R                           
## version.string R version 3.6.1 (2019-07-05)
## nickname       Action of the Toes

R Packages

In order to reproduce the analysis, several R packages, which you can install with the following code:

# list of packages on CRAN used in this project (exclusing base packages)
pkg <- c("brglm", 
         "brglm2", 
         "clusterGeneration",
         "devtools",
         "doParallel", 
         "doRNG",
         "foreach", 
         "ggraph",
         "gridExtra", 
         "gridExtra", 
         "igraph",
         "kableExtra",
         "logistf", 
         "quantreg", 
         "scoring", 
         "texreg", 
         "tidyverse", 
         "xtable")

To install these packages, you can run the code above along with the command below.

install.packages(pkg, repos = "http://cran.rstudio.com")

You also need the package separation from GitHub, which you can install with the command below.

devtools::install_github("carlislerainey/separation")

We recommend using the latest version of each package, but the versions we used are saved to the file package-versions.csv.

library(tidyverse)
library(kableExtra)

devtools::package_info(pkgs = c(pkg, "separation"), dependencies = TRUE) %>%
  select(package, version = ondiskversion, date, source) %>%
  write_csv("package-versions.csv")

Reproducing without Make

  • To create makefile-dag.png, run the R script makefile-dag.R.
  • To do the simulations for figures 2-5 and store them as simulations/simulations.rds, run the R script R/simulations.R.
  • To create the figures based on the simulations above, run the R script R/plot-simulations.R. These figures are stored as .pdfs in manuscript/figs.
  • To perform the sample size simulations for figure 8 and store them as simulations/sample-size-simulations.rds, run the R script R/sample-size-simulations.R.
  • To create the figures based on the sample size simulations above, run R/plot-sample-size-simulations.R. These figures are stored as .pdfs in manuscript/figs.
  • To reproduce the George and Epstein re-analysis, run the R script ge-replication/R/analysis.R. Figures are stored as .pdfs in manuscript/figs.
  • To reproduce the Weisiger re-analysis, run the R script weisiger-replication/R/analysis.R. Figures are stored as .pdfs in manuscript/figs.
  • To compile the manuscript and appendix, compile manuscript/small.tex and manuscript/small-appendix.tex, respectively, with pdftex and bibtex.
  • To render the README.md, compile README.Rmd with knitr.
  • To render the computed-values.pdf, compile computed-values.pdf with knitr.

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Practical advice for logistic regression with a small sample

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