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. ______________________________________________ [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.
