
glmmPQL  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,
        control, niter = 10, verbose = TRUE, ...)
 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, list or environment used as the first place to find variables in the formulae, weights and if present in ..., subset.  
 
correlation  an optional correlation structure.  
 
weights  optional case weights as in glm.  
 
control  an optional argument to be passed to lme.  
 
niter  maximum number of iterations.  
 
verbose  logical: print out record of iterations?  
 
...  Further arguments for lme.  

 Details glmmPQL works by repeated calls to lme, so package nlme will be loaded at first use if necessary.  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.  Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.  See Also lme  Examples 
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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