# MCMCglmm

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##### Multivariate Generalised Linear Mixed Models

Markov chain Monte Carlo Sampler for Multivariate Generalised Linear Mixed Models with special emphasis on correlated random effects arising from pedigrees and phylogenies (Hadfield 2010). Please read the course notes: vignette("CourseNotes", "MCMCglmm") or the overview vignette("Overview", "MCMCglmm")

Keywords
models
##### Usage
MCMCglmm(fixed, random=NULL, rcov=~units, family="gaussian", mev=NULL,
data,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, singular.ok=FALSE, saveX=TRUE,
saveZ=TRUE, saveXL=TRUE, slice=FALSE, ginverse=NULL, trunc=FALSE)
##### Arguments
fixed

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

random

formula for the random effects. Multiple random terms can be passed using the + operator, and in the most general case each random term has the form variance.function(formula):linking.function(random.terms). Currently, the only variance.functions available are idv, idh, us, cor[] and ante[]. idv fits a constant variance across all components in formula. Both idh and us fit different variances across each component in formula, but us will also fit the covariances. corg fixes the variances along the diagonal to one and corgh fixes the variances along the diagonal to those specified in the prior. cors allows correlation submatrices. ante[] fits ante-dependence structures of different order (e.g ante1, ante2), and the number can be prefixed by a c to hold all regression coefficients of the same order equal. The number can also be suffixed by a v to hold all innovation variances equal (e.g antec2v has 3 parameters). The formula can contain both factors and numeric terms (i.e. random regression) although it should be noted that the intercept term is suppressed. The (co)variances are the (co)variances of the random.terms effects. Currently, the only linking.functions available are mm and str. mm fits a multimembership model where multiple random terms are separated by the + operator. str allows covariances to exist between multiple random terms that are also separated by the + operator. In both cases the levels of all multiple random terms have to be the same. For simpler models the variance.function(formula) and linking.function(random.terms) can be omitted and the model syntax has the simpler form ~random1+random2+.... There are two reserved variables: units which index rows of the response variable and trait which index columns of the response variable

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-response model might have the R-structure defined by ~us(trait):units

family

optional character vector of trait distributions. Currently, "gaussian", "poisson", "categorical", "multinomial", "ordinal", "threshold", "exponential", "geometric", "cengaussian", "cenpoisson", "cenexponential", "zipoisson", "zapoisson", "ztpoisson", "hupoisson", "zibinomial", "threshold" and nzbinom are supported, where the prefix "cen" means censored, the prefix "zi" means zero inflated, the prefix "za" means zero altered, the prefix "zt" means zero truncated and the prefix "hu" means hurdle. If NULL, data needs to contain a family column.

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 elements as random effect components. The fourth element QUASI should be logical: if TRUE starting latent variables are obtained heuristically, if FALSE then they are sampled from a Z-distribution

prior

optional list of prior specifications having 3 possible elements: R (R-structure) G (G-structure) and B (fixed effects). B is a list containing the expected value (mu) and a (co)variance matrix (V) representing the strength of belief: the defaults are B$mu=0 and B$V=I*1e+10, where where I is an identity matrix of appropriate dimension. The priors for the variance structures (R and G) are lists with the expected (co)variances (V) and degree of belief parameter (nu) for the inverse-Wishart, and also the mean vector (alpha.mu) and covariance matrix (alpha.V) for the redundant working parameters. The defaults are nu=0, V=1, alpha.mu=0, and alpha.V=0. When alpha.V is non-zero, parameter expanded algorithms are used.

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 burn-in phase has finished.

pedigree

ordered pedigree with 3 columns id, dam and sire or a phylo object. This argument is retained for back compatibility - see ginverse argument for a more general formulation.

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 only, and for pedigrees a vector of ids can be passed to nodes specifying the subset of individuals for which animal effects are estimated. Note that all analyses are equivalent if omitted nodes have missing data but by absorbing these nodes the chain max mix better. However, the algorithm may be less numerically stable and may iterate slower, especially for large phylogenies.

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

singular.ok

logical: if FALSE linear dependencies in the fixed effects are removed. if TRUE they are left in an estimated, although all information comes form the prior

saveX

logical: save fixed effect design matrix

saveZ

logical: save random effect design matrix

saveXL

logical: save structural parameter design matrix

slice

logical: should slice sampling be used? Only applicable for binary traits with independent residuals

ginverse

a list of sparse inverse matrices (${\bf A^{-1}}$) that are proportional to the covariance structure of the random effects. The names of the matrices should correspond to columns in data that are associated with the random term. All levels of the random term should appear as rownames for the matrices.

trunc

logical: should latent variables in binary models be truncated to prevent under/overflow (+/-20 for categorical/multinomial models and +/-7 for threshold/probit models)?

##### Value

Sol

Posterior Distribution of MME solutions, including fixed effects

VCV

Posterior Distribution of (co)variance matrices

CP

Posterior Distribution of cut-points from an ordinal model

Liab

Posterior Distribution of latent variables

Fixed

list: fixed formula and number of fixed effects

Random

list: random formula, dimensions of each covariance matrix, number of levels per covariance matrix, and term in random formula to which each covariance belongs

Residual

list: residual formula, dimensions of each covariance matrix, number of levels per covariance matrix, and term in residual formula to which each covariance belongs

Deviance

deviance -2*log(p(y|...))

DIC

deviance information criterion

X

sparse fixed effect design matrix

Z

sparse random effect design matrix

XL

sparse structural parameter design matrix

error.term

residual term for each datum

family

distribution of each datum

Tune

(co)variance matrix of the proposal distribution for the latent variables

meta

logical; was mev passed?

##### References

General analyses: Hadfield, J.D. (2010) Journal of Statistical Software 33 2 1-22

Phylogenetic analyses: Hadfield, J.D. & Nakagawa, S. (2010) Journal of Evolutionary Biology 23 494-508

Background Sorensen, D. & Gianola, D. (2002) Springer

##### See Also

mcmc

• MCMCglmm
##### Examples
# NOT RUN {
# Example 1: univariate Gaussian model with standard random effect

data(PlodiaPO)
model1<-MCMCglmm(PO~1, random=~FSfamily, data=PlodiaPO, verbose=FALSE,
nitt=1300, burnin=300, thin=1)
summary(model1)

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

data(bird.families)

phylo.effect<-rbv(bird.families, 1, nodes="TIPS")
phenotype<-phylo.effect+rnorm(dim(phylo.effect)[1], 0, 1)

# simulate phylogenetic and residual effects with unit variance

test.data<-data.frame(phenotype=phenotype, taxon=row.names(phenotype))

Ainv<-inverseA(bird.families)$Ainv # inverse matrix of shared phyloegnetic history prior<-list(R=list(V=1, nu=0.002), G=list(G1=list(V=1, nu=0.002))) model2<-MCMCglmm(phenotype~1, random=~taxon, ginverse=list(taxon=Ainv), data=test.data, prior=prior, verbose=FALSE, nitt=1300, burnin=300, thin=1) plot(model2$VCV)

# }

Documentation reproduced from package MCMCglmm, version 2.29, License: GPL (>= 2)

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