Andrew Perrin <[EMAIL PROTECTED]> writes: > Sorry for my ignorance, but could you explain a little further? I'm > guessing from your response that this makes the log-likelihood that is > quoted by glmmPQL a poor measure of model fit. Are there are statistics > that would be better for reporting model fit?
You could try GLMM from the lme4 package instead. It has two methods of fitting the model - PQL and Laplace. The parameter estimates from PQL should be similar to those from glmmPQL (it's essentially the same algorithm) but the log-likelihood reported by GLMM is that evaluated by the Laplacian approximation. If you choose method="Laplace" then GLMM does the PQL fit followed by further iterations to optimize the (second-order) Laplacian approximation to the log-likelihood. This takes longer to fit but should be more accurate. The log-likelihood from this fit should be greater than that from the PQL fit for the same model. These log-likelihood can be compared between models. ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
