
Fit a GLMM model with multivariate normal random effects, using Penalized Quasi-Likelihood.
glmmPQL(fixed, random, family, data, correlation, weights,
control, niter = 10, verbose = TRUE, …)
a two-sided linear formula giving fixed-effects part of the model.
a formula or list of formulae describing the random effects.
a GLM family.
an optional data frame used as the first place to find
variables in the formulae, weights
and if present in
…
, subset
.
an optional correlation structure.
optional case weights as in glm
.
an optional argument to be passed to lme
.
maximum number of iterations.
logical: print out record of iterations?
Further arguments for lme
.
A object of class "lme"
: see lmeObject
.
glmmPQL
works by repeated calls to lme
, so
package nlme
will be loaded at first use if necessary.
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.
# NOT RUN {
library(nlme) # will be loaded automatically if omitted
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
# }
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