Proc glimmix data = cool plots=all; Class a1 a2 a3 a4; Model a1 = a2/ dist = poisson  s ddfm = kr; Random a3/ subject = a4 residual type = cs; run; Is it normal when I run this, the conditonal variance of residuals looks more constant than when I run it without the residual option? So residual is essentially an overdispersion parameter to account for the variance? Also is residual the same as _residual_? ... View more

So can you still how would you interpret the residual covariance estimate for a poisson regressiion? ... View more

I actually run it like this and it says R-side random effects now allowed: Proc glimmix data = blah plots=residualpanel; Class a1 a2 a3; Model ff = a1/ dist = poisson  s; random a2/ subject = a3 residual type = cs;; random a2; run; ... View more

But then what does the residual mean in the covariance parameter estimates when you have R-side random effects? Is it meaningless? ... View more

So the Pearson residuals should show a normal distribution? ... View more