# PC priors for residual correlation parameters in one-factor mixed models

## TL;DR

This paper introduces penalized complexity priors for residual correlation parameters in one-factor Bayesian mixed models, enabling better shrinkage towards independence and consistent scaling across models, demonstrated through a community ecology case study.

## Contribution

It develops a unified PC prior framework for correlation parameters in one-factor mixed models, facilitating practical prior specification and model comparison.

## Key findings

- PC priors effectively shrink towards independence
- Prior scaling depends only on variance explained by groups
- Application shows improved model comparison in ecology

## Abstract

Lack of independence in the residuals from linear regression motivates the use of random effect models in many applied fields. We start from the one-way anova model and extend it to a general class of one-factor Bayesian mixed models, discussing several correlation structures for the within group residuals. All the considered group models are parametrized in terms of a single correlation (hyper-)parameter, controlling the shrinkage towards the case of independent residuals (iid). We derive a penalized complexity (PC) prior for the correlation parameter of a generic group model. This prior has desirable properties from a practical point of view: i) it ensures appropriate shrinkage to the iid case; ii) it depends on a scaling parameter whose choice only requires a prior guess on the proportion of total variance explained by the grouping factor; iii) it is defined on a distance scale common to all group models, thus the scaling parameter can be chosen in the same manner regardless the adopted group model. We show the benefit of using these PC priors in a case study in community ecology where different group models are compared.