[R] GLM and normality of predictors

[Ben Bolker]

Sacha Viquerat <tweedie-d <at> web.de> writes:

> 
> Am 15.04.2011 20:14, schrieb Christian Hennig:
> > Normality of the predictors doesn't belong to the assumptions of the
> > GLM, so you don't have to check this.
> >
> > On Fri, 15 Apr 2011, Simone Santoro wrote:
> >
> >> I want to estimate the possible effects of some predictors on my
> >> response variable that is n? of males and n? of females
> >> (cbind(males,females)), so, it would be:
> >>
> >> fullmodel<-glm(cbind(males,females)~pred1+pred2+pred3, binomial)
> >>
> if you count no of males and females, shouldn't you choose the poisson 
> family? maybe whoever you told you to check for normality referred to 
> that, since count data are not normally distributed (neither are their 
> errors)! maybe thats all he/she wants!


I think the original model using the binomial distribution
for the response seems entirely appropriate.

  I agree with the comment about tiny data sets:
the usual rule of thumb is that (# parameters) should 
be <(effective N)/10 -- so in practice estimating
anything more than a single binary or continuous predictor (both
of which require a single parameter to estimate) would be pushing
it.

  (Sad but true.)

  Ben Bolker