Reducing Incidence of Nonpositive Definite Covariance Matrices in Mixed Effect Models.

Deciding which random effects to retain is a central decision in mixed effect models. Recent recommendations advise a maximal structure whereby all theoretically relevant random effects are retained. Nonetheless, including many random effects often leads to nonpositive definiteness. A typical remedy is to simplify the random effect structure by removing random effects or associated covariances. However, this practice is known to bias estimates of remaining covariance parameters and compromise fixed effect inferences. Cholesky decompositions frequently are suggested as an alternative and are automatically implemented in some software. Instead of Cholesky decompositions, we describe factor analytic structures as an approach to avoid nonpositive definiteness. This approach is occasionally employed in biosciences like plant breeding, but, ironically, has not been established in behavioral sciences despite the close historical connection with factor analysis in these fields. We discuss how a factor analytic structure facilitates estimation and conduct simulations to compare convergence and performance to simplifying the random effects structure or Cholesky decomposition approaches. Results show a lower rate of nonpositive definiteness with the factor analytic structure than Cholesky decomposition and suggest that factor analytic covariance structure may be useful to combating nonpositive definiteness, especially in models with many random effects.