glmmPQL {MASS}R Documentation

Fit Generalized Linear Mixed Models via PQL

Description

Fit a GLMM model with multivariate normal random effects, using PQL.

Usage

glmmPQL(fixed, random, family, data, correlation, control, ...)

Arguments

fixed a two-sided linear formula object describing the 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 variable sin the formulae.
correlation an optional correlation structure.
control an optional argument to be passed to lme. (This is modified in the S version by passed unchanged in the R version.)
... Further arguments for lme.

Details

glmmPQL is a wrapper to iterative calls to lme. Initial values are found by a call to glm, and then lme is applied to the working vector, including the BLUPs of the random effects in forming the linear predictor.

Value

A object of class "lme": see lmeObject.

Author(s)

B. D. Ripley

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

require(nlme)

data(bacteria)
summary(glmmPQL(y ~ trt + I(week> 2), random = ~ 1 | ID, 
                  family = binomial, data = bacteria))
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