Malin Pinsky <malin.pinsky@...> writes: > I'm having problems fitting a mixed-effects model for an ecological > meta-analysis, and I'm curious if anyone has advice. In particular, > it's pretty clear that the variance in the residuals increases with > the predicted mean, but my normal fixes don't seem to be working. The > model is: > > mod1 <- lmer(logCd ~ logRe + Hab + logRe:Hab + (logRe|Study), data=temp) > > where Cd is a drag coefficient (>0 before log-transformation), Re is a > physical quantity called a Reynolds number (also >0 before > transformation), Hab is a categorical variable for habitat, and Study > is a categorical variable for the study the data came from. I know > from fluid dynamics theory that logCd and logRe can be linearly > related, but I expect that the slope and intercept vary between > habitat types and between studies.
[big snip to make gmane happy] > And, if this belongs on the R-sig-ME list, let me know. Probably. A quick answer is that you should able to incorporate heteroscedasticity in lme (from the nlme function) via something like weights=varPower(): mod1 <- lme(logCd ~ logRe*Hab, random=~logRe|Study, data=temp, weights=varPower()) (this might not be quite right, you might want to read ?nlme::varPower and/or the relevant bit of Pinheiro and Bates 2000) If you want to go the Gamma route, you can try (1) the development version of lme4 (install from r-forge, but it might be broken right now ...); (2) glmmADMB ... ... but I would probably suggest lme for now. _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
