lmer and method call

Hello all,

I'm attempting to fit a generalized linear mixed-effects model using lmer
(R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call:

vidusLMER1 <- lmer(jail ~ visit + gender + house + cokefreq + cracfreq +
herofreq + borcur + comc + (1 | code), data = vidusGD, family = binomial,
correlation = corCompSymm(form = 1 | ID), method = "ML")

Although the model fits, the summary indicates the model is a "Generalized
linear mixed model fit using Laplace". I've tried any number of
permutations; is only Laplace supported in lmer, despite the text of the
help file?

The help file does say that for a generalized linear mixed model
(GLMM), which is what family = binomial implies, the estimation
criterion is always "ML" (maximum likelihood) as opposed to "REML"
(restricted, or residual, maximum likelihood).  So stating method =
"ML" is redundant.

For a GLMM, however, the log-likelihood cannot not be evaluated
directly and must be approximated.  Here the help file is misleading
because it implies that there are three possible approximations, "PQL"
(penalized quasi-likelihood), "Laplace" and "AGQ" (adaptive Gaussian
quadrature).  AGQ has not yet been implemented so the only effective
choices are PQL and Laplace.  The default is PQL, to refine the
starting estimates, followed by optimization of the Laplace
approximation.  In some cases it is an advantage to suppress the PQL
iterations which can be done with one of the settings for the control
argument.