I do not understand the term "mexval statistics".
I think you want to look for "anova.glm", fitting several models
leaving each term out one at a time in succession and then using
"anova.glm" to compare your general model with each submodel in
succession. If that does NOT give you what you want, please ask again,
AFTER first reading the posting guide
"http://www.R-project.org/posting-guide.html";; And please provide
commented, minimal, self-contained, reproducible code with your post,
explaining in particular why "anova.glm" does not seem to solve your
problem.
There is a problem with SEE in non-normal situations, if by SEE
you mean standard error of the estimate. Least squares with normal
errors is also maximum likelihood. The consensus among professional
statisticians has long been that when the the errors are not additive or
normal or independent or have constant variance, the proper
generalization is to use maximum likelihood, provided one can select an
appropriate likelihood. In particular, "glm" assumes independent
binomial observations. If that is NOT reasonable, you should not be
using "glm".
Hope this helps.
Spencer Graves
Mihai Nica wrote:
Greetings:
I would like to kindly ask help with obtaining mexval statistics (marginal
explanatory value - percentage increase in SEE if the variable were left out of
the regression model) for a logit (glm) model with several continuous
independent variables. I believe I can do it manually for each variable, but I
really hope there might be somebody who has a function already written. Writing
one is still a little over my skills (I am working on it though).
Thanks,
mike
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