Linear mixed effect model
On 11-03-19 10:47 AM, Manuel Sp?nola wrote:
Hi Ben and other list members, I looked at the residuals and log transforming the gave me heteroscedasticity, so I don't know if I need to transform. Is statistically appropriate to fit different models, lienear, gls, lme and compare them with AIC? mod1 = lm(Swiftness.1 ~ Lure + Sex + Facility.Size, data = otter) mod2 = gls(Swiftness.1 ~ Lure + Sex + Facility.Size, data = otter) mod3 = gls(Swiftness.1 ~ 1, data = otter) mod4 = lme(Swiftness.1 ~ Lure + Sex + Facility.Size, random = ~1|Subject, data = otter)
AICctab(mod1, mod2, mod3, mod4, weights = T, delta = TRUE, base = T,
sort = TRUE, nobs = 17)
AICc df dAICc weight
mod2 1276.4 10 0.0 1
mod4 1294.5 11 18.1 <0.001
mod3 1302.9 2 26.6 <0.001
mod1 1356.3 10 80.0 <0.001
A few thoughts: * you can in principle compare various models (including those with/without random effects), but it is a crude approximation for several reasons (boundary issues with random effects, marginal vs conditional AIC, etc. -- see <http://glmm.wikidot.com/faq> for more discussion). * Take a look at very recent discussions on this list about comparing lm vs gls vs lme; in particular make sure you have REML=TRUE/FALSE set appropriately. As you have done it, the fits may not be comparable. * I think you should retain the random effect of 'otter' in any case because it is a natural part of the experimental design (although I think that if you correctly set REML=FALSE you will get identical likelihoods between gls and lme, and gls will appear better because it is missing a random-effect variance parameter)