You are right that the data are extremely overdispersed. However, I have not been able to get Negative Binomial models to converge with my data. I have not investigated the generalized Poisson - but that is a great suggestion. As for the R-side random effects, this was a suggestion from the SAS technical support, as I had trouble with model convergence with G-side effects. Yes, DAP is a continuous variable for tree diameter. Although I have more variables available, our model is predicting fruit production per tree as a function of year, DAP, and site. (I did not list the other possible covariates to simplify the problem.) There is some evidence in previous literature of a more quadratic pattern of diameter versus numbers of fruits (due to tree senescence); however, my data do not support that. There is a weak linear relationship evident in the data. Since there are years where some trees produce no fruit at all, then a log-normal model is not appropriate. These are Brazil nut trees and the fruit production can vary wildly from year to year, with some individuals producing 900+ fruits and some producing 0. 'Normal' production is in the 100-200 range. We are trying to better explain variation among trees and among years. I had avoided the Laplace and quad methods as I was interested in using Kenward-Rogers DDFM. That said, I have followed your suggestion and got some output. For the first set of code, I get a Pearson Chi-sq/DR of 41. absolutely terrible. For the second, the Pearson Chi-sq/DF many of the effects are not estimable. PROC GLIMMIX DATA=sasds.cast_1019 method=quad ;
CLASS local arv ano ;
MODEL prod = local|dap|ano /DIST=poisson ;
random intercept ano / subject=arv(local);
RUN; Any suggestions as to why that might happen? here is the output: Fit Statistics for Conditional Distribution
-2 log L(prod | r. effects) 12826.77
Pearson Chi-Square 353.17
Pearson Chi-Square / DF 0.15
Covariance Parameter Estimates
Cov Parm Subject Estimate Standard
Error
Intercept Arv(local) 2.4705 0.2474
ano Arv(local) 1.1158 0.04375
Type III Tests of Fixed Effects
Effect Num DF Den DF F Value Pr > F
local 1 256 2.16 0.1428
dap 1 0 3.42 .
dap*local 1 0 1.85 .
ano 8 2012 7.38 <.0001
local*ano 8 2012 1.46 0.1654
dap*ano 8 0 3.12 .
dap*local*ano 8 0 0.77 .
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