Hi everybody, I'm trying to analyse a set of data with a non-normal response, 2 fixed effects and 1 nested random effect with strong heteroscedasticity in the model.
I planned to use the function lmer : lmer(resp~var1*var2 + (1|rand)) and then use permutations based on the t-statistic given by lmer to get p-values. 1/ Is it a correct way to obtain p-values for my variables ? 2/ I read somewhere that lme is more adequate when heteroscedasticity is strong. Do I have to use lme instead of lmer ? 3/ It is possible to fit a glm in lmer using family="...". Is it possible to use it in spite of hard heteroscedasticity ? 4/ A last question concerning SAS. My model appears to not converge in SAS, indicating a "structure" in the variance. Is it implying something in lmer or lme ? Many Thanks -- Alan Juilland -- Alan Juilland – PhD Student Department of Ecology and Evolution Biophore, University of Lausanne 1015 Dorigny Switzerland Tel : ++41 21 692 41 74 Fax : +41 21 692 41 65 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
