MCMCglmm (version 1.06)

MCMCglmm: Multivariate Generalised Linear Mixed Models

Description

Markov chain Monte Carlo Sampler for Multivariate Generalised Linear Mixed Models with special emphasis on correlated random effects arising from pedigrees and phylogenies. Please read the tutorial: vignette("Tutorial", "MCMCglmm")

Usage

MCMCglmm(fixed, random=NULL, rcov=~units, family="gaussian", mev=NULL, 
    data=NULL,start=NULL, prior=NULL, tune=NULL, pedigree=NULL,
    nodes="ALL", scale=TRUE, nitt=13000, thin=10, burnin=3000, pr=FALSE,
    pl=FALSE, verbose=TRUE, DIC=TRUE)

Arguments

fixed

formula for the fixed effects, multiple responses are passed as a matrix using cbind

random

formula for the random effects. There are three reserved variables: units which indexes rows of the response variable, trait which indexes columns of the response variable and

rcov

formula for residual covariance structure. This has to be set up so that each data point is associated with a unique residual. For example a multi-trait model might have the R-structure defined by

family

optional character vector of trait distributions. Currently, "gaussian", "poisson", "categorical", "multinomial", "exponential", "cengaussian", "cenpoisson"

mev

optional vector of measurement error variances for each data point for random effect meta-analysis.

data

data.frame

start

optional list having 4 possible elements: R (R-structure) G (G-structure) and liab (latent variables or liabilities) should contain the starting values where G itself is also a list with as many elem

prior

optional list of prior specifications having 3 possible elements: R (R-structure) G (G-structure) and B (fixed effects). Each element is a list containing the expected (co)variances (V) and a degree

tune

optional (co)variance matrix defining the proposal distribution for the latent variables. If NULL an adaptive algorithm is used which ceases to adapt once the burnin phase has finished.

pedigree

ordered pedigree with 3 columns id, dam and sire or a phylo object.

nodes

pedigree/phylogeny nodes to be estimated. The default, "ALL" estimates effects for all individuals in a pedigree or nodes in a phylogeny (including ancestral nodes). For phylogenies "TIPS" estimates effects for the tips onl

scale

logical: should the phylogeny (needs to be ultrametric) be scaled to unit length (distance from root to tip)?

nitt

number of MCMC iterations

thin

thinning interval

burnin

burnin

pr

logical: should the posterior distribution of random effects be saved?

pl

logical: should the posterior distribution of latent variables be saved?

verbose

logical: if TRUE MH diagnostics are printed to screen

DIC

logical: if TRUE deviance and deviance information criterion are calculated

Value

• Sol
  Posterior Distribution of MME solutions, including fixed effects
• VCV
  Posterior Distribution of (co)variance matrices
• Liab
  Posterior Distribution of latent variables
• Fixed
  formula: fixed terms
• Random
  formula: random terms
• Residual
  formula: residual terms
• Deviance
  deviance -2*log(p(y|...))
• DIC
  deviance information criterion

References

For phylogenetic analyses: Hadfield & Nakagawa (2009) submitted For other analyses: Hadfield in prep Sorensen & Gianola (2004) Springer

See Also

mcmc

Examples

# Example 1: univariate gaussian model with standard random effect
 
data(PlodiaPO)  
model1<-MCMCglmm(PO~1, random=~FSfamily, data=PlodiaPO, verbose=FALSE)
plot(model1$VCV)

# Example 2: univariate gaussian model with phylogenetically correlated random effect

data(bird.families) 

phylo.effect<-rbv(bird.families, 1, nodes="TIPS") # simulate phylogenetic effects with unit variance
phenotype<-phylo.effect+rnorm(dim(phylo.effect)[1], 0, 1)  # add residual with unit variance

test.data<-data.frame(phenotype=phenotype, animal=as.factor(row.names(phenotype)))

model2<-MCMCglmm(phenotype~1, random=~animal, data=test.data, pedigree=bird.families, verbose=FALSE)
plot(model2$VCV)