MCMCglmm-package: Multivariate Generalised Linear Mixed Models
Description
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 & Gianola (2002) and Davis
(2006) which taken together result in what is hopefully a fast and efficient
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 Rice's
(2004) tensor analysis to real data and functions for visualising and comparing
matrices.
Please read the tutorial: vignette("Tutorial", "MCMCglmm")References
Sorensen & Gianola (2002) Likelihood, Bayesian and MCMC Methods in Quantitative
Genetics
Davis (2006) Direct Methods for Sparse Linear Systems
Rice (2004) Evolutionary Theory: Mathematical and Conceptual Foundations