Summarising GLMM Fits from MCMCglmm

summary method for class "MCMCglmm". The returned object is suitable for printing with the print.summary.MCMCglmm method.

Usage

# S3 method for class 'MCMCglmm'
summary(object, random=FALSE, ...)

Arguments

object

an object of class "MCMCglmm"

random

logical: should the random effects be summarised

...

Further arguments to be passed

Value

DIC

Deviance Information Criterion

fixed.formula

model formula for the fixed terms

random.formula

model formula for the random terms

residual.formula

model formula for the residual terms

solutions

posterior mean, 95% HPD interval, MCMC p-values and effective sample size of fixed (and random) effects

Gcovariances

posterior mean, 95% HPD interval and effective sample size of random effect (co)variance components

Gterms

indexes random effect (co)variances by the component terms defined in the random formula

Rcovariances

posterior mean, 95% HPD interval and effective sample size of residual (co)variance components

Rterms

indexes residuals (co)variances by the component terms defined in the rcov formula

csats

chain length, burn-in and thinning interval

cutpoints

posterior mean, 95% HPD interval and effective sample size of cut-points from an ordinal model

theta_scale

posterior mean, 95% HPD interval, MCMC p-values and effective sample size of scaling parameter in theta_scale models.

Author

Jarrod Hadfield j.hadfield@ed.ac.uk

See also

MCMCglmm