Hi all!
I hope somebody can help me solve some doubts which must be very basic, but I haven't been able to solve by myself. The first one, is how to assess for overdispersion in GlmmPQL when fitting binomial or poisson errors. The second one is whether GlmmPQL can compare models with different fixed effects. The third doubt, regards the way I should arrange my data in a GlmmPQL with binomial errors. In glm, I am supposed to create cbind vector joining the "number of successes" and the "total-the number of successes". Should I proceed the same way for GlmmPQL of can I use a single column which, intead of containing the numbers, simply contains 0 or 1?. The reason for this question, is that I am trying to fit a variance components analysis with a single random effect and no fixed effects. The only way I know to test for the significance of the single level of random effects is by comparing the model with a glm without fixed effects and do a ChiSquare test. So, should the data of both models be arranged the same way? or is it possible to compare the model with random effects and response "0,1" whith that of a glm without fixed effects where the response is arranged as cbind(successes,total-successes)? My concern of using cbind in GlmmPQL, is that lose replication of the variable of interest, and I have realized that when fitting the random variable as a fixed effect in a simple glm, I end up with 0 residual degrees of freedom. Thanks in advance for your help, Olga ______________________________________________ [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
