vcov.glmerMod {merDeriv}

Extract Variance-Covariance Matrix of all Parameters for Generalized Linear Mixed Effects Models

Description

This function calculates the variance-covariance matrix for all parameters (fixed and random effect) in a generalized linear mixed effects model of class glmerMod.

Usage

## S3 method for class 'glmerMod'
vcov(object, ...)

Arguments

object

An object of class glmerMod.

...

additional arguments, including full and ranpar (full = FALSE and ranpar = "var" are default; see details).

Value

A p by p variance-covariance matrix, where p represents the number of parameters. If full = FALSE, returns the variance-covariance matrix of only fixed effect parameters. If full = TRUE , returns the variance-covariance matrix for all fitted parameters (including fixed effect parameters and random effect (co)variances). The variance-covariance matrix is based on the negative of Hessian matrix, which is extracted from lme4. If ranpar = "var", the random effects are parameterized as variance/covariance; If ranpar = "sd", the random effects are parameterized as standard deviation/correlation; If ranpar = "theta", the random effects are parameterized as components of Cholesky decomposition.

References

Douglas Bates, Martin Maechler, Ben Bolker, Steve Walker (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1-48. doi: 10.18637/jss.v067.i01.

Examples

## Not run: 
# The cbpp example
data(finance, package="smdata")

lme4fit <- glmer(corr ~ jmeth + (1 | item), data = finance,
                 family = binomial, nAGQ = 20)

# variance covariance matrix for all parameters
vcov(lme4fit, full = TRUE, ranpar = "var")

## End(Not run)