O Tosas Auguet
s0129600 at sms.ed.ac.uk
Wed Jun 9 11:05:46 CEST 2004
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
Could someone bring light to this?
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