lme4 (version 0.6-2)

GLMM: Fit Generalized Linear Mixed Models via PQL

Description

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

Usage

GLMM(formula, family, data, random, ...)

Arguments

formula

a two-sided linear model formula giving fixed-effects part of the model.

family

a GLM family, see glm.

data

an optional data frame used as the first place to find variables in the formulae.

random

A formula or named list of formulae describing the random effects.

...

Optional further arguments such as subset and na.action.

Value

• An object of class "lme": see ssclme-class.

synopsis

GLMM(formula, family, data, random, method = c("PQL", "Laplace"), control = list(), subset, weights, na.action, offset, model = TRUE, x = FALSE, y = FALSE, ...)

Details

Additional arguments, some of them standard in model-fitting functions, can be passed to GLMM. [object Object],[object Object],[object Object],[object Object],[object Object]

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.

See Also

lme

Examples

data(guImmun)
fm1 <-
    GLMM(immun ~ kid2p + mom25p + ord + ethn +
                 momEd + husEd + momWork + rural + pcInd81,
         family = binomial, data = guImmun, random = ~1|comm)
summary(fm1)