Hierarchical generalized linear models: the R package HGLMMM. 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.
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References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Casals, Martí; Langohr, Klaus; Carrasco, Josep Lluís; Rönnegård, Lars: Parameter estimation of Poisson generalized linear mixed models based on three different statistical principles: a simulation study (2015)
- Gałecki, Andrzej; Burzykowski, Tomasz: Linear mixed-effects models using R. A step-by-step approach (2013)
- Gning, Lucien Diégane; Pierre-Loti-Viaud, Daniel: On the existence of maximum likelihood estimators in Poisson-gamma HGLM and negative binomial regression model (2013)
- Molas, Marek; Noh, Maengseok; Lee, Youngjo; Lesaffre, Emmanuel: Joint hierarchical generalized linear models with multivariate Gaussian random effects (2013)
- Marek Molas; Emmanuel Lesaffre: Hierarchical Generalized Linear Models: The R Package HGLMMM (2011) not zbMATH