Generalized Linear Mixed-Effects Models in R
Final rept. 16 Aug 2002-15 Jan 2003
TOYON RESEARCH CORP GOLETA CA
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The Nonlinear and Linear Mixed-Effects NLME package for the open source statistical software system R provides an effective and efficient way to analyze longitudinal data collected from nested groups of subjects when the response of interest is on a continuous scale. It does not provide methods for analyzing binary, multinomial, or ordinal responses, where a general linear mixed-effects model GLMM is required. We enhanced an existing R implementation for estimating a GLMM, which can estimate an approximate model using a crude numerical procedure. The R code was rewritten to take advantage of the best available numerical methods and the latest theoretical developments. Using simulated data sets, we demonstrate that the enhanced code is much faster and numerically robust. We propose an approach for modeling ordinal and multinomial data, the theory of which is less well developed than that of binomial data. The proposed approach is demonstrated using simulated and real data sets. The results illustrate the limits of the approximate procedure used in Phase I, which motivates the use of a more refined numerical method in Phase II.
- Statistics and Probability