I am using PROC GLIMMIX to analyse a continuous response variable that is dependent on 4 independent categorical variables..
Each of the 4 independent variables has two levels.
The first independent categorical variable is a soil type (soilA, soilB)
The second independent categorical variable is a plant type (strawberry, raspberry)
The third independent categorical variable is a color type (blue, red)
The fourth is a light type (IR, NIR)
The response variable is the plant fluorescence.
My syntax looks like this:
proc glimmix;
class soil color plant light;
model FL = plant soil / solution;
random intercept / subject=light;
lsmeans plant / cl ilink;
run;
The results that I get do not look right.
What I am obtaining are values of my standard error that are all similar.
Kindly assist me.
@JAO1 wrote:
The results that I get do not look right.
What I am obtaining are values of my standard error that are all similar.
Could you please show us the results?
Could you explain why similar standard errors seems to be a problem?
@JAO1 wrote:
Based on the examples in the GLIMMIX procedure, standard errors are dissimiliar, yet all of the ones that I have calculated for my treatment groups are the same.
I'm afraid this doesn't convince me.
The results in this example from the GLIMMIX Procedure as well as other examples always result in dissimilar Standard Errors.
What am I doing wrong?
Please show us YOUR results
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