Reverse-Bayes Methods for Replication Studies

This repository contains all files for reproducing the PhD thesis from Samuel Pawel. The thesis is based on a heavily modified version of the LaTeX thesis template from Sebastian Meyer (GPL2 license). My template contains some ugly hacks, use it at your own risk!

1. Reproducing the thesis locally

Make sure that LaTeX (e.g., texlive-full on Ubuntu), R, and the R packages in CRANpackages.txt are installed

## install packages from CRAN by running from a shell
R -e 'install.packages(read.delim("CRANpackages.txt", header = FALSE)[,1])'

Then run

make local

this should produce thesis.pdf.

Although the analyses depend on only few dependencies, this approach may lead to different results (or not even compile successfully) in the future if R or the packages change. The R and R package versions which were used when the thesis was successfully compiled the last time are visible in the following output

sessionInfo()

#> R version 4.2.2 Patched (2022-11-10 r83330)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.5 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=de_CH.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=de_CH.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=de_CH.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=de_CH.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] UpSetR_1.4.0           dplyr_1.0.10           ReplicationSuccess_1.2
#> [4] xtable_1.8-4           scales_1.2.1           gridExtra_2.3         
#> [7] ggplot2_3.4.0          knitr_1.40            
#> 
#> loaded via a namespace (and not attached):
#>  [1] Rcpp_1.0.9       pillar_1.8.1     compiler_4.2.2   plyr_1.8.7      
#>  [5] highr_0.9        tools_4.2.2      evaluate_0.18    lifecycle_1.0.3 
#>  [9] tibble_3.1.8     gtable_0.3.1     pkgconfig_2.0.3  rlang_1.0.6     
#> [13] DBI_1.1.3        cli_3.4.1        xfun_0.34        withr_2.5.0     
#> [17] stringr_1.4.1    generics_0.1.3   vctrs_0.5.0      grid_4.2.2      
#> [21] tidyselect_1.2.0 glue_1.6.2       R6_2.5.1         fansi_1.0.3     
#> [25] farver_2.1.1     magrittr_2.0.3   assertthat_0.2.1 colorspace_2.0-3
#> [29] labeling_0.4.2   utf8_1.2.2       stringi_1.7.8    munsell_0.5.0

cat(paste(Sys.time(), Sys.timezone(), "\n"))

#> 2022-12-20 16:18:45 Europe/Zurich

2. Reproducing the thesis within a Docker container

Make sure that Docker with root rights is installed. Then run

make docker

This may take some time as TinyTeX needs to install several LaTeX packages (run make docker2 to compile only R code within the container but run LaTeX locally). The Docker approach reruns the analyses in a Docker container which encapsulates the computational environment (R and R package versions) that was used in the original analysis. The only way this approach could become non-reproducible is when the rocker/verse base image becomes unavailable and/or the MRAN snapshot of CRAN becomes unavailable.

Reproducing the individual papers

The thesis contains six papers, .tex and figure output files for each of them are already included in the repository. Code and data to reproduce them individually can be found at

1. The assessment of replication success based on relative effect size: https://github.com/SamCH93/RSgolden/

2. The sceptical Bayes factor for the assessment of replication success: https://gitlab.uzh.ch/samuel.pawel/BFScode and https://gitlab.uzh.ch/samuel.pawel/BayesRep

3. Bayesian approaches to designing replication studies: https://github.com/SamCH93/BAtDRS and https://github.com/SamCH93/BayesRepDesign

4. Reverse-Bayes methods for evidence assessment and research synthesis https://gitlab.uzh.ch/samuel.pawel/Reverse-Bayes-Code

5. Comment on "Bayesian additional evidence for decision making under small sample uncertainty": https://github.com/SamCH93/BAEcomment

6. Pitfalls and Potentials in Simulation Studies: https://github.com/SamCH93/SimPaper and https://github.com/LucasKook/ainet

Archives of the git repositories are also provided in source/paper-source.