
   Hello, Sverre:

   I would like to add something to your question. I have heard that one of the best features about glmm-s is that they can deal extremely well with issues such as non-normality of the data, indeed much better than any other statistical packages. If that's true and trying to translate it into a more pragmatic rule, I would say that, as long as your response variable is continuous, you can, as a general rule, choose "family=gaussian" and lmer will deal quite well with the data as far as the distribution is not extremely skewed or strongly divergent from a normal one. And further to that, binomial for proportion data and Poisson for count data, as a general rule, of course, but with many other options and possibilities more adapted to more specific purposes. 

   I know it seems like a sort of answer, but I'm actually asking if this point of view is correct, as an add-on to Sverre's question.

   Thank you very much!

   Iker   

--- El dom, 6/3/11, Sverre Stausland <johnsen@fas.harvard.edu> escribió:

De: Sverre Stausland <johnsen@fas.harvard.edu>
Asunto: [R-sig-ME] Guidelines for choosing family (and link) in lmer
Para: r-sig-mixed-models@r-project.org
Fecha: domingo, 6 de marzo, 2011 21:41

Hi all,

I was wondering whether there are any general guidelines out there
(online or in the literature) explaining how to choose the appropriate
call for "family" in the lmer function?

Right now, I have a data set where the dependent variable "looks
normal", but where shapiro.test and ks.test tell me it's not. It's not
clear to me whether I should still use "family=gaussian" or use
something else. (I'm not giving details here, because I am looking for
general guidelines).

Best
Sverre

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