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Hello- I noted the excellent exchange of info. back in 2012 on how to obtain accurate estimates of LSMEANS and standard errors using PROC GLIMMIX when the response variable has been log-transformed. Steve- thanks for the suggestion. However, I tried this approach but found no improvement in my residuals (non-constant variance problem). In a nutshell, here is my conceptual linear model:
ln (x...a continuous resp.variable) = f (y, z)...where y is continuous and z is a 3-level class variable. So the GLIMMIX program statement with the "fix" for back-transforming the LSMEANS would be:
PROC GLIMMIX DATA=example plots=all ;
CLASS z;
MODEL x= y z/SOLUTION LINK=LOG;
LSMEANS z/CL DIFF ILINK;
RUN;
The resulting residuals display no improvement and are essentially the same as the output from a non-transformed model without the link=log statement. I am doubtless making a stupid mistake! I would appreciate any help you can offer. Thanks.
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Hi,
I'm working with a survey dataset of skewed fasting glucose level, when logn (fgl) transformed it is normally distributed. I need to use PROC MIXED. How can I back transform the LSMEANS and its standard error for the log-transformed data?
Thanks in advance.
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Please read all of the above, and consider using PROC GLIMMIX instead.
Steve Denham
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