R: Fit Generalized Linear Mixed Models via PQL

glmmPQL {MASS}

R Documentation

Fit Generalized Linear Mixed Models via PQL

Description

Fit a GLMM model with multivariate normal random effects, using Penalized Quasi-Likelihood.

Usage

glmmPQL(fixed, random, family, data, correlation, weights,
        control, niter = 10, verbose = TRUE, ...)

Arguments

`fixed`	a two-sided linear formula giving fixed-effects part of the model.
`random`	A formula or list of formulae describing the random effects.
`family`	a GLM family.
`data`	an optional data frame used as the first place to find variables in the formulae.
`correlation`	an optional correlation structure.
`weights`	optional case weights as in `glm`.
`control`	an optional argument to be passed to `lme`.
`niter`	maximum number of iterations.
`verbose`	logical: print out record of iterations?
`...`	Further arguments for `lme`.

Details

glmmPQL works by repeated calls to lme, so package nlme will be loaded at first use if necessary.

Value

A object of class "lme": see lmeObject.

References

Schall, R. (1991) Estimation in generalized linear models with random effects. Biometrika 78, 719–727.

Breslow, N. E. and Clayton, D. G. (1993) Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9–25.

Wolfinger, R. and O'Connell, M. (1993) Generalized linear mixed models: a pseudo-likelihood approach. Journal of Statistical Computation and Simulation 48, 233–243.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

library(nlme) # will be loaded automatically if omitted
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
                family = binomial, data = bacteria))

[Package Contents]