Hierarchical Generalized Linear Models: The R Package HGLMMM

Marek Molas, Emmanuel Lesaffre

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)

Abstract

The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution. The distribution of random effects can be specified as Gaussian, gamma, inverse-gamma or beta. Complex structures as multi-membership design or multilevel designs can be handled. Further, dispersion parameters of random components and the residual dispersion (overdispersion) can be modeled as a function of covariates. Overdispersion parameter can be fixed or estimated. Fixed effects in the mean structure can be estimated using extended likelihood or a first order Laplace approximation to the marginal likelihood. Dispersion parameters are estimated using first order adjusted profile likelihood.
Original languageUndefined/Unknown
Pages (from-to)1-20
Number of pages20
JournalJournal of Statistical Software
Volume39
Issue number13
Publication statusPublished - 2011

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