[R] GLM and normality of predictors

From: Simone Santoro <miseno77_at_hotmail.com>
Date: Fri, 15 Apr 2011 19:04:40 +0200

Hi,

I have found quite a few posts on normality checking of response variables, but I am still in doubt about that. As it is easy to understand I'm not a statistician so be patient please. I want to estimate the possible effects of some predictors on my response variable that is nš of males and nš of females (cbind(males,females)), so, it would be:

fullmodel<-glm(cbind(males,females)~pred1+pred2+pred3, binomial)

I have n= 11 (ecological data, small sample size is a a frequent problem!).

Someone told me that I have to check for normality of the predictors (and in case transform to reach normality) but I am in doubt about the fact that a normality test can be very informative with such a small sample size. Also, I have read that a normality test (Shapiro, Kolmogornov, Durbin, etc.) can't tell you anything about the fact that the distribution is normal but just that there is no evidence for non-normality. Anyway, I am still looking for some sort of thumb of rule to be used in these cases.

The question: is there some simple advice on the way one should proceed in this cases to be reasonably confident of the results?

Thanks for any help you might provide                                                

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