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Hierarchical Generalized Linear Models: The R Package HGLMMM

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  • Molas, Marek
  • Lesaffre, Emmanuel

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.

Suggested Citation

  • Molas, Marek & Lesaffre, Emmanuel, 2011. "Hierarchical Generalized Linear Models: The R Package HGLMMM," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i13).
  • Handle: RePEc:jss:jstsof:v:039:i13
    DOI: http://hdl.handle.net/10.18637/jss.v039.i13
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    Cited by:

    1. Gning, Lucien & Diagne, M.L. & Tchuenche, J.M., 2023. "Hierarchical generalized linear models, correlation and a posteriori ratemaking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).
    2. Walt Stroup & Elizabeth Claassen, 2020. "Pseudo-Likelihood or Quadrature? What We Thought We Knew, What We Think We Know, and What We Are Still Trying to Figure Out," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 639-656, December.

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