Title
Likelihood-Based Inference for Generalized Linear Mixed Models: Inference with the R Package glmm
Department/School
Statistics
Date
2021
Document Type
Article
DOI
https://doi.org/10.1002/sta4.339
Abstract
The R package glmm enables likelihood‐based inference for generalized linear mixed models with a canonical link. No other publicly available software accurately conducts likelihood‐based inference for generalized linear mixed models with crossed random effects. glmm is able to do so by approximating the likelihood function and two derivatives using importance sampling. The importance sampling distribution is an essential piece of Monte Carlo likelihood approximation, and developing a good one is the main challenge in implementing it. The package glmm uses the data to tailor the importance sampling distribution and is constructed to ensure finite Monte Carlo standard errors. In the context of the generalized linear mixed model, the salamander model with crossed random effects has become a benchmark example. We use this model to illustrate the complexities of the likelihood function and to demonstrate the use of the R package glmm.
Volume
10
Issue
1
Published in
Stat
Citation/Other Information
Knudson, C., Benson, S., Geyer, C., & Jones, G. (2021). Likelihood-based inference for generalized linear mixed models: Inference with the R package glmm. Stat, 10(1). https://doi.org/10.1002/sta4.339