Dear all,
I have two questions concerning model simplification in GlmmPQL, for for random and fixed effects: 1. Fixed effects: I don't know if I can simply specify anova(model) and trust the table that comes up with the p value for each variable in the fixed effects formula. I have read that the only way to test for fixed effects is to do approximate wald tests based on the standard errors of the models where I am subsequently withdrawing one variable from the fixed effect formula at a time. What does "aproximate" wald test mean? What is the best option? 2. Random effects: If AIC is not meaningful in GlmmPQL, how do I test for the significance of the random effects? 3. I way to see if 1 single level of random effects is helpful in terms of analysing the data, would be to comapre the GlmmPQL model with a glm models without random effects, but again: what do I compare if AIC is not meaninful? and if there is something I can compare, could I test for the significance of that difference? Could someone bring light to this? Thanks, Olga Tosas ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html