Hello all, With the help of SAS for Mixed Models, I am still slowly learning to interpret results generated by PROC GLIMMIX for a simple RCBD. I am trying to figure out how to correctly present my results. I am not sure whether I should be presenting transformed or original parameters. Because my data consists of counts my rationale for using PROC GLIMMIX was that it would allow me to specify a non-normal distribution and interpret, discuss and present parameters for untransformed data but now I am not so sure. Background: RCBD, 1 treatment, 2 levels (vegetated walls and un-vegetated walls). 10 green walls, 10 blank walls (equivalent to 20 observational units). Vacuum sampled insects from within 10 quadrats (subsamples) on each green wall and 3 on each blank wall. Testing if green walls have significantly higher number of insects than blank walls during three separate months of sampling. I am using the p-value presented in the Type III Test of Fixed Effects to determine whether the difference between the means of insects present in the two treatments was significant. It was, with p<0.0001 Traditionally, to present my results I would create a bar graph showing the means of the treatments, their standard errors, and letters indicating significant differences between the two means. I am not sure if this is appropriate anymore. What I think I should be presenting is the significant difference between the means of log transformed predicted values generated by a model based on my original data. Should I then be showing these means instead? If so, I assume I would use the values under the Estimates column generated by the LSMEANS statement. But do these values have “real world” meaning? I realize that I could use ILINK statement to back transform but these values are still quite different from means calculated from my original data. In a nutshell I am wondering if it is appropriate to present the means and standard errors of my original data although the significant differences detected are based on these transformed expected parameters… I hope this question makes sense. Thank you for any thoughts or suggestions. Sincerely, Serena Code below is for sampling in August: data abundancevisit3withsub; input blk trt$ subsample y; lines; 1 g 1 1 1 g 2 4 1 g 3 5 1 g 4 4 1 g 5 0 1 g 6 14 1 g 7 3 1 g 8 7 1 g 9 2 1 g 10 4 1 b 11 0 1 b 12 0 1 b 13 0 2 g 1 9 2 g 2 8 2 g 3 4 2 g 4 5 2 g 5 3 2 g 6 9 2 g 7 5 2 g 8 6 2 g 9 2 2 g 10 1 2 b 11 1 2 b 12 0 2 b 13 0 3 g 1 2 3 g 2 0 3 g 3 1 3 g 4 0 3 g 5 0 3 g 6 1 3 g 7 0 3 g 8 0 3 g 9 0 3 g 10 2 3 b 11 0 3 b 12 0 3 b 13 0 4 g 1 5 4 g 2 0 4 g 3 2 4 g 4 2 4 g 5 0 4 g 6 4 4 g 7 4 4 g 8 3 4 g 9 5 4 g 10 14 4 b 11 1 4 b 12 . 4 b 13 0 5 g 1 62 5 g 2 16 5 g 3 28 5 g 4 30 5 g 5 63 5 g 6 57 5 g 7 61 5 g 8 45 5 g 9 36 5 g 10 24 5 b 11 0 5 b 12 1 5 b 13 2 6 g 1 4 6 g 2 18 6 g 3 7 6 g 4 8 6 g 5 10 6 g 6 18 6 g 7 15 6 g 8 18 6 g 9 4 6 g 10 10 6 b 11 0 6 b 12 0 6 b 13 0 7 g 1 19 7 g 2 43 7 g 3 24 7 g 4 34 7 g 5 11 7 g 6 20 7 g 7 38 7 g 8 85 7 g 9 47 7 g 10 85 7 b 11 1 7 b 12 0 7 b 13 0 8 g 1 3 8 g 2 3 8 g 3 3 8 g 4 4 8 g 5 5 8 g 6 6 8 g 7 11 8 g 8 7 8 g 9 5 8 g 10 2 8 b 11 1 8 b 12 0 8 b 13 0 9 g 1 1 9 g 2 0 9 g 3 1 9 g 4 2 9 g 5 15 9 g 6 3 9 g 7 3 9 g 8 3 9 g 9 4 9 g 10 4 9 b 11 1 9 b 12 1 9 b 13 1 10 g 1 9 10 g 2 2 10 g 3 6 10 g 4 6 10 g 5 5 10 g 6 13 10 g 7 18 10 g 8 8 10 g 9 5 10 g 10 16 10 b 11 0 10 b 12 0 10 b 13 0; proc print data=abundancevisit3withsub; run; proc glimmix data=abundancevisit3withsub method=quad; class trt blk subsample; model y = trt / solution dist=negbin link=log; random int trt / sub=blk; lsmeans trt/ilink; ods select LSMeans Estimates; run;
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