We did an experiment where we grew mushrooms on two species of tree logs across three different farms. The mushrooms were harvested and weighed with logs inoculated the first year harvested twice and logs inoculated the second year harvested once.  Code and output below. Why is fixed effect estimate zero and it cannot estimate lsmeans for oak and tallow, even thought it does for oak and tallow by inoculation and harvest year?   ods graphics on; proc sort data= mushroom.data; by TreeSp innocyear harvestyear; proc glimmix data=mushroom.data2; class Farm TreeSp TreeID innocyear harvestyear; model Weight_Cum= TreeSp innocyear harvestyear innocyear*harvestyear*TreeSp /ddfm=kr2 solution; random intercept /subject =Farm; random TreeID(TreeSp) /group=farm; lsmeans TreeSp innocyear*harvestyear*TreeSp/ilink lines /*pdiff=all CL*/; run; quit;   ... View more

Hi, I am analyzing data from an experiment testing whether insects from northern latitudes are more cold-hardy that those from southern latitudes. I have five large cages in each of three locations (north, mid, south) Each cage has 21 potted plants on which I placed 10 insects from north, mid, or southern populations( 21= 3 sources of insects x 7 months). Each month I remove one potted plant per source from each cage and count what proportion of insects survived (The variable prop is a calculated variable equal to live insects/found insects). The code that I think is the best so far is below. I have also tried binomial, which had worse fit statistics and neg binomial, which didn’t converge.   proc glimmix data=puncv.new plots=pearsonpanel (conditional marginal); class Month Location Source Cage; model prop= Month Location Source ; random intercept /subject=Cage; run;   When I run this, I get the below fit statistics and residual graphs? What is the generalized chi-sq/df= 0.05 mean about how the model fits? Is there a model that would produce better residual graphs (i.e. fit the data better?)   ... View more