anova on binomial LMER objects
The issues with lmer and the analysis of variance are due to its not make appropriate correction for the prior weights vector. If you convert your binomial response to the equivalent number of binary responses you get an appropriate anova table. It's on the "ToDo" list to fix this but a few other things have to come first, like grading assignments in one of my courses and repairing the computer in my office. This is the third motherboard I have torched in four months.
On 9/29/05, Alan Cobo-Lewis <alanc at umit.maine.edu> wrote:
On Wed, 28 Sep 2005, Robert Bagchi wrote:
Hi Patrick thanks for your advice. I have now tried glmmPQL, and it worked fine - I'm getting consistent results between plots and models fitted by glmmPQL. Plus it allows predict() and resid() which is another advantage over lmer at present. quick question though: why does one need to use PQL for binomial models? Is there a good reference for this?
You don't have to use PQL for binomial models, but you can't use least-squares. PQL is an approximate solution. Laplace and Adaptive Gaussian Quadrature options in lmer are better approximations. So lmer would likely become the better option as it progresses in its development (though the current issues you've found with the F ratios certainly sound like maybe lmer isn't better for you in its current incarnation). alan -- Alan B. Cobo-Lewis, Ph.D. (207) 581-3840 tel Department of Psychology (207) 581-6128 fax University of Maine Orono, ME 04469-5742 alanc at maine.edu http://www.umaine.edu/visualperception
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