On Dec 1, 2007 9:26 AM, Douglas Bates <[EMAIL PROTECTED]> wrote: > On Nov 29, 2007 8:09 PM, M-J Milloy <[EMAIL PROTECTED]> wrote: > > > > 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.
I forgot to mention that the correlation argument has no effect in this call. That argument is for the lme function in the nlme package. In lmer it is ignored. ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
