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,
        dispersion = NULL, control, niter = 10, verbose = T, ...)

REQUIRED 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.
dispersion
the over-dispersion of the GLMM: by default this is estimated but can be specified here.
control
an optional argument to be passed to lme. (This is modified in the S version but passed unchanged in the R version.)
niter
maximum number of iterations.
verbose
logical: print out record of iterations?
...
Further arguments for lme.

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.

SEE ALSO:

EXAMPLES:

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