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;
