Abstract
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 <b>MCMCglmm</b> 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 <b>CSparse</b> library for sparse linear systems.
Keywords
Markov chain Monte CarloCategorical variableStatisticsMathematicsRange (aeronautics)GaussianApplied mathematicsPoisson distributionAlgorithmLinear regressionComputer scienceRandom effects modelLatent variableMarkov chainMonte Carlo method
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Publication Info
- Year
- 2010
- Type
- article
- Volume
- 33
- Issue
- 2
- Citations
- 4603
- Access
- Closed
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Cite This
Jarrod D. Hadfield
(2010).
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The<b>MCMCglmm</b><i>R</i>Package.
Journal of Statistical Software
, 33
(2)
.
https://doi.org/10.18637/jss.v033.i02
Identifiers
- DOI
- 10.18637/jss.v033.i02