Deciding which predictor effects may vary across subjects is a difficult issue. Standard model selection criteria and test procedures are often inappropriate for comparing models with different numbers of random effects, due to constraints on the parameter space of the variance components. Testing on the boundary of the parameter space changes the asymptotic distribution of some classical test statistics and causes problems in approximating Bayes factors. We propose a simple approach for testing random effects in the linear mixed model using Bayes factors. We scale each random effect to the residual variance, and introduce a parameter that controls the relative contribution of each random effect, free of the scale of the data. We integrate out the random effects and the variance components using closed form solutions. The resulting integrals needed to calculate the Bayes factor are low-dimensional integrals lacking variance components, and can be efficiently approximated with Laplace’s method. We propose a default prior distribution on the parameter controlling the contribution of each random effect, and conduct simulations to show that our method has good properties for model selection problems. Finally, we illustrate our methods on data from a clinical trial of patients with bipolar disorder and on data from an environmental study of water disinfection by-products and male reproductive outcomes.


Biostatistics | Longitudinal Data Analysis and Time Series | Statistical Methodology | Statistical Theory