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?)
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