MCMCglmm package for R

January 29, 2009

(This article was first published on Gregor Gorjanc, and kindly contributed to R-bloggers)

Jarrod Hadfield published MCMCglmm package on CRAN. The package can fit generalised linear mixed models via MCMC methods. Bellow is the abstract from the vignette. The list of supported models is quite impressive. Nice job Jarrod! This is not the first package by Jarrod – there is also interesting (at least to me) package MasterBayes.
MCMCglmm is a package for fitting Generalised Linear Mixed Models using Markov chain Monte Carlo techniques. Most commonly used distributions like the normal and the Poisson are supported together with some useful but less popular ones like the zero-inflated Poisson and the multinomial. Missing values and left, right and interval censoring are accommodated for all traits. The package also supports multi-trait models where the multiple responses can follow different types of distribution. The package allows various residual and random effect variance structures to be specified including heterogeneous variances, unstructured covariance matrices and random regression (e.g. random slope models). Three special types of variance structure that can be specified are those associated with pedigrees (animal models), phylogenies (the comparative method) and measurement error (meta-analysis). The package makes heavy use of results in Sorensen and Gianola [2002] and Davis [2006] which taken together result in what is hopefully a fast and effcient routine. Most small to medium sized problems should take seconds to a few minutes, but large problems (> 20,000 records) are possible. My interest is in evolutionary biology so there are also several functions for applying tensor analysis [Rice, 2004] to real data and functions for visualising and comparing matrices.

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