mcvcov: Monte Carlo Variance Covariance Matrix

In glmm: Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation

Monte Carlo Variance Covariance Matrix

Description

A function that calculates the Monte Carlo variance covariance matrix for the Monte Carlo maximum likelihood estimates returned from glmm.

Usage

mcvcov(object)

Arguments

object

An object of class glmm usually created using glmm.

Details

With maximum likelihood performed by Monte Carlo likelihood approximation, there are two sources of variability: there is variability from sample to sample and from Monte Carlo sample (of generated random effects) to Monte Carlo sample. The first source of variability (from sample to sample) is measured using standard error, which appears with the point estimates in the summary tables. The second source of variability is due to the Monte Carlo randomness, and this is measured by the Monte Carlo standard error.

A large entry in Monte Carlo variance covariance matrix indicates the Monte Carlo sample size m is too small.

The square root of the diagonal elements represents the Monte Carlo standard errors. The off-diagonal entries represent the Monte Carlo covariance.

Value

mcvcov

The Monte Carlo variance covariance matrix for the Monte Carlo maximum likelihood estimates returned from glmm

Author(s)

Christina Knudson

References

Geyer, C. J. (1994) On the convergence of Monte Carlo maximum likelihood calculations. Journal of the Royal Statistical Society, Series B, 61, 261–274. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.2517-6161.1994.tb01976.x")}.

Knudson, C. (2016) Monte Carlo likelihood approximation for generalized linear mixed models. PhD thesis, University of Minnesota. http://hdl.handle.net/11299/178948

See Also

glmm for model fitting.

Examples

library(glmm)
data(BoothHobert)
set.seed(1234)
mod <- glmm(y~0+x1, list(y~0+z1), varcomps.names=c("z1"), 
data=BoothHobert, family.glmm=bernoulli.glmm, m=100, doPQL=TRUE)

mcvcov(mod)

glmm documentation built on Sept. 30, 2024, 9:44 a.m.