stanova: User-Friendly Interface and Summaries for Bayesian Statistical Models Estimated with Stan
Psychological data often consists of multiple orthogonal factors. When analyzing such data with statistical models these factors, like all categorical variables, need to be transformed into numerical covariates using a contrast scheme. To the surprise of many users, the default contrast scheme in the statistical programming language R is such that the intercept is mapped onto the first factor level with the consequence that in models with interactions, coefficients represent simple effects at the first factor level instead of the usually expected average effects. I will present a software package for R, stanova (https://github.com/bayesstuff/stanova), that allows estimating statistical models in a Bayesian framework based on Stan and package rstanarm that avoids this problem. It by default uses a factor coding proposed by Rouder et al. (2012, JMP) in which the intercept corresponds to the unweighted grand mean and which allows priors that have the same marginal prior on all factor levels. In addition, stanova provides a summary method which reports results for each factor level or design cell – specifically the difference from the intercept – instead of for each model coefficient. This also provides a better user experience than the default output of many statistical packages. The talk will show the implementation of the package in R and its adaptation in JASP, an open source alternative to SPSS.
Wow, very cool work here! I especially appreciate how you demonstrated the stanova package’s usage and output in such a clear way. I don’t often stray from my MATLAB workflow, but am definitely planning to for this :) Thanks for the great talk!
stanova: https://github.com/bayesstuff/stanova Links to R Script: http://singmann.org/download/r/stanova_mathpsych2020.Rmd http://singmann.org/download/r/stanova_mathpsych2020.html Powerpoint Slides: http://singmann.org/download/publications/presentations/MathPsych2020-stanova.pptx