skip to main content
Idioma:
Primo Search
Clear Search Box
Search in: Busca Geral
Busca Avançada
Busca por Índices

MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package

Hadfield, Jarrod D.

Journal of statistical software, 2010-02, Vol.33 (2) [Periódico revisado por pares]

Foundation for Open Access Statistics

Texto completo disponível

Citações Citado por
  • Título:
    MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package
  • Autor: Hadfield, Jarrod D.
  • Assuntos: animal model ; linear mixed model ; MCMC ; multivariate ; pedigree ; phylogeny ; sparse
  • É parte de: Journal of statistical software, 2010-02, Vol.33 (2)
  • Descrição: Generalized linear mixed models provide a flexible framework for modeling a range of data, although with non-Gaussian response variables the likelihood cannot be obtained in closed form. Markov chain Monte Carlo methods solve this problem by sampling from a series of simpler conditional distributions that can be evaluated. The R package MCMCglmm implements such an algorithm for a range of model fitting problems. More than one response variable can be analyzed simultaneously, and these variables are allowed to follow Gaussian, Poisson, multi(bi)nominal, exponential, zero-inflated and censored distributions. A range of variance structures are permitted for the random effects, including interactions with categorical or continuous variables (i.e., random regression), and more complicated variance structures that arise through shared ancestry, either through a pedigree or through a phylogeny. Missing values are permitted in the response variable(s) and data can be known up to some level of measurement error as in meta-analysis. All simu- lation is done in C/ C++ using the CSparse library for sparse linear systems.
  • Editor: Foundation for Open Access Statistics
  • Idioma: Inglês
Voltar para lista de resultados

  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

Buscando em bases de dados remotas. Favor aguardar.