GLMM {lme4}R Documentation

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.

Details

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

subset
an optional expression indicating the subset of the rows of data that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.
method
character: Estimation method to be used. Possible values are "PQL", the default, or "Laplace". "PQL" provides penalized quasi-likelihood estimates. "Laplace" provides PQL estimation followed by optimization of the second-order Laplacian approximation to the marginal log-likelihood.
na.action
a function that indicates what should happen when the data contain NAs. The default action (na.fail) causes lme to print an error message and terminate if there are any incomplete observations.
control
an optional argument to be passed to lme.
model, x
logicals. If TRUE the corresponding components of the fit (the model frame, the model matrices) are returned.

Value

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

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)

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