summary.MCMCglmm: Summarising GLMM Fits from MCMCglmm

In MCMCglmm: MCMC Generalised Linear Mixed Models

Summarising GLMM Fits from MCMCglmm

Description

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(s)

Jarrod Hadfield [e-mail]

See Also

MCMCglmm

MCMCglmm documentation built on July 9, 2023, 5:24 p.m.