spencerg spencer.graves at prodsyse.com
Mon May 18 15:28:56 CEST 2009

      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 

      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
> 	[[alternative HTML version deleted]]
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

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