Robert Kubinec January 11th, 2022

Please note: This is the repository for the paper files. To access the R package ordbetareg, please go to this repository: www.github.com/saudiwin/ordbetareg_pack.

This repository contains data and code for the paper, “Ordered Beta Regression: A Parsimonious, Well-Fitting Model for Continuous Data with Lower and Upper Bounds", which is now forthcoming at the journal Political Analysis. An ungated preprint can be found here: https://osf.io/preprints/socarxiv/2sx6y/. Replication files can be found both on Dataverse and Github.

To replicate the paper, please first run the install.R script to make sure all relevant packages are installed. The script will also install cmdstanr and a version of cmdstan, which is the underlying MCMC sampling library from the Stan project. Installing cmdstan requires the R toolchain; if you have any trouble or are unsure see the cmdstanr package installation instructions: https://mc-stan.org/cmdstanr/articles/cmdstanr.html.

The file master.R will then run all the necessary scripts to compile the paper and supplementary information (compilation requires a working Latex installation). Note that master.R by default loads the existing simulation data in the data folder. To fully reproduce the simulation, set the run_sim variable in master.R to TRUE. Note that running the full simulation can require up to a few days on a machine with ~40 cores.

The version of R used for these results was 4.1.2 and the version of the R packages is as follows:

• dplyr: 1.0.7
• rstanarm: 2.21.1
• tidyr: 1.1.4
• lubridate: 1.8.0
• loo: 2.4.1
• kableExtra: 1.3.4
• bayesplot: 1.8.1
• patchwork: 1.1.1
• stringr: 1.4.0
• grDevices: 4.1.2
• emojifont: 0.5.5
• latex2exp: 0.5.0
• haven: 2.4.3
• ggplot2: 3.3.5
• posterior: 1.2.0
• brms: 2.16.3
• remotes: 2.4.2
• future.apply: 1.8.1
• faux: 1.1.0
• rmarkdown: 2.11
• bookdown: 0.24
• tinytex: 0.36
• extrafont: 0.17

The repository includes the following files:

• kubinec_ord_betareg_accepted.Rmd The accepted version of the reproducible Rmarkdown document that can be run in Rstudio to re-produce the results. Note that the data folder in this repository contains necessary data to reproduce results.
• estimate_with_brms.Rmd and estimate_with_brms.html These files show how to run ordered beta regression models using the R package brms.
• define_ord_betareg.R This R script contains all the auxiliary code needed to fit the model with R package brms (see vignette above for more info).
• *_fit.rds Fitted model object files to reproduce paper results much faster
• data/sim_cont_X*.RData Simulation results to reproduce paper results much faster
• data/suffrage_paper_replicationfiles/EER-D-13-00718R2_maindata_suffrage.dta Data from Toke and Aidt (2012)
• ordered_beta_reg_sim.R This R script will run a simulation comparing the ordered beta regression model to alternatives, including the zero-one-inflated Beta regression model (ZOIB). The output of a 10,000 run of this simulation is saved in data/ as sim_cont_X.RData.
• ordered_beta_reg_sim_fixed.R This R script will run a simulation comparing the ordered beta regression model to alternatives, but with fixed rather than random draws of relevant parameters (results are in the SI, not main paper). The output of a 4,000 run of this simulation is saved in data/ as sim_cont_X_fixed.RData.
• beta_logit.stan This file contains the Stan code used to fit an ordered beta regression model in Stan.
• zoib_nophireg.stan This file contains Stan code used to fit the zero-one-inflated beta regression model (ZOIB).
• beta_logit_phireg.stan This file constains Stan code to fit an ordered beta regression model with additional predictors for phi, the scale parameter in the distribution. These additional parameters allow for understanding the effect of covariates on encouraging clustered or more dispersed (estreme) responses from respondents.
• frac_logit.stan This file contains a Stan parameterization of the fractional logit model.
• beta_logit_infl*.stan These additional Stan files are various ways of parameterizing the midpoint of the scale when the midpoint is considered missing data. None of them appear to do a better job at predicting the outcome than versions that considered the midpoint to be observed data.
• BibTexDatabase.bib References necessary to compile the paper.
• preamble.tex Latex packages for paper