How would you back transform values that were a result of dist=beta link=logit?
ods graphics on;
proc glimmix data=nocheck method=laplace plots=studentpanel; by site;
class site trt rep;
model d3 = trt / dist=beta link=logit;
random intercept / subject=rep;
store equirate;
run;
ods graphics off;
There are two scales for predictions in generalized linear models: the data scale and the linear scale. The ILINK function tells you how to transform betwen the scales.
If you use the LOGIT function for the link, then the inverse-link function is the LOGISTIC function. For details and further discussion, see "Predicted values in generalized linear models: The ILINK option in SAS."
You might specify which values.
Depending there are places where there are options like ILINK that computes items in terms of the data and not the link function.
There are two scales for predictions in generalized linear models: the data scale and the linear scale. The ILINK function tells you how to transform betwen the scales.
If you use the LOGIT function for the link, then the inverse-link function is the LOGISTIC function. For details and further discussion, see "Predicted values in generalized linear models: The ILINK option in SAS."
Thanks for the link to the document on Ilink options!
So, according to the document, the inverse of the logit is described as follows:
The inverse of the logit function is called the logistic function:
g-1(η) = logistic(η) = 1 / (1 + exp(-η))
So, using this, to back transform the estimate in Excel, I would use the following equation?:
=1/((1+EXP(-estimate)))
This seems to make sense as the back transformed values are very similar to the raw means.
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