Hi. First of all, I do not consider myself being a stats-guru and my "extended" SAS and stats-knowledge is mostly self-tought. My question deals with the SAS procedure GLIMMIX and, more precisely, the generated output table. My research question is related to animal sciences and with my statistical analysis I am trying to identify how different feeds and ingredients may interact with one another affect animal performance (my response variable).   I thought of analyzing my data using a full factorial GLMM. I am having 2 class variables and I came up with the following model:   ods graphics on; proc glimmix Data=Data2 plots =pearsonpanel; class RefDiet TestIngr; model ADCprotI=RefDiet TestIngr RefDiet*TestIngr / dist=beta link=logit solution; lsmeans RefDiet*TestIngr / adjust=bon ilink oddsratio; run; ods graphics off;   The beta distribution was chosen according to the residual panel, as it seems to provide a better fit and be more appropriate than a gaussian-shaped distribution (response variables are expressed as proportions with values between 0 and 1). With one specific response variable I am now getting standard errors of 0 for all class variables, resulting in infinite t values. As a result, all class variables turn out to likely have a highly significant effect on my response variable. However, something seems wrong here, but I am currently stumped... Does anyone has a good suggestion on how to overcome this issue or am I doing something completely wrong here? Please let me know if you need more information + thanks a lot!     The result for e.g. fixed effects looks like this (lines should be labelled with: RefDiet TestIngr RefDiet*TestIngr)   Type III Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F 1 16 Infty <.0001 3 16 Infty <.0001 3 16 Infty <.0001     And for the parameter estimates (just the upper lines, the shown line names are: RefDiet 1   RefDiet 2   TestIngr   10 TestIngr   20 TestIngr   30...)     Parameter Estimates Effect RefDiet TestIngr Estimate Standard Error DF t Value Pr > |t| 2.1766 0 16 Infty <.0001 2.4569 0 16 Infty <.0001 -0.4995 0 16 -Infty <.0001 -0.1410 0 16 -Infty <.0001 -0.01848 0 16 -Infty <.0001 ... View more