# summary.MCMCglmm

##### Summarising GLMM Fits from MCMCglmm

`summary`

method for class `"MCMCglmm"`

. The returned object is suitable for printing with the `print.summary.MCMCglmm`

method.

- Keywords
- models

##### Usage

```
# S3 method for 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

Deviance Information Criterion

model formula for the fixed terms

model formula for the random terms

model formula for the residual terms

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

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

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

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

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

chain length, burn-in and thinning interval

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

##### See Also

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