Psychoco 2021: Ting Wang - merDeriv: Derivative Computations for GLMMs
Channel: Achim Zeileis
Date: 2021-02-05

Invited presentation from Psychoco 2021 online (https://www.psychoco.org/2021/)

Title: merDeriv: Derivative Computations for Generalized Linear Mixed Effects Models with Application
Author: Ting Wang
Affiliation: University of Missouri

Abstract: While likelihood-based derivatives and related facilities are available in R for many types of statistical models, the facilities have been notably lacking for models estimated via lme4. This is because the necessary statistical output, including the Hessian, Fisher information and casewise contributions to the model gradient, is not immediately available from lme4 and is not trivial to obtain. In this presentation, we describe merDeriv, an R package which supplies new functions to obtain analytic output from Gaussian mixed models as well as quadrature method for generalized linear mixed model with one cluster. After describing the computation methods, we illustrate a variety of possible uses of these derivatives that will be familiar to many Psychoco attendees. These uses include score tests of fixed effect parameters, heterogeneity in random effects and likelihood ratio tests of non-nested models.