Modelling monotonic effects of ordinal predictors in Bayesian regression models

Paul Christian Bürkner*, Emmanuel Charpentier

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
109 Downloads (Pure)

Abstract

Ordinal predictors are commonly used in regression models. They are often incorrectly treated as either nominal or metric, thus under- or overestimating the information contained. Such practices may lead to worse inference and predictions compared to methods which are specifically designed for this purpose. We propose a new method for modelling ordinal predictors that applies in situations in which it is reasonable to assume their effects to be monotonic. The parameterization of such monotonic effects is realized in terms of a scale parameter b representing the direction and size of the effect and a simplex parameter sigma modelling the normalized differences between categories. This ensures that predictions increase or decrease monotonically, while changes between adjacent categories may vary across categories. This formulation generalizes to interaction terms as well as multilevel structures. Monotonic effects may be applied not only to ordinal predictors, but also to other discrete variables for which a monotonic relationship is plausible. In simulation studies we show that the model is well calibrated and, if there is monotonicity present, exhibits predictive performance similar to or even better than other approaches designed to handle ordinal predictors. Using Stan, we developed a Bayesian estimation method for monotonic effects which allows us to incorporate prior information and to check the assumption of monotonicity. We have implemented this method in the R package brms, so that fitting monotonic effects in a fully Bayesian framework is now straightforward.

Original languageEnglish
Pages (from-to)420-451
Number of pages32
JournalBRITISH JOURNAL OF MATHEMATICAL AND STATISTICAL PSYCHOLOGY
Volume73
Issue number3
Early online date1 Jan 2020
DOIs
Publication statusPublished - Nov 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Bayesian statistics
  • brms
  • isotonic regression
  • ordinal variables
  • R
  • Stan

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