I'm performing some linear mixed models for a psychological experiment. I'm not a statistician so my knowledge is limited.
The basic idea is that: I have an experiment in which I model my response variable as a function of within-subject factors (
Mem) and a between-subject factor (
Group). I use nlme:
fit <- lme(Score ~ Group + Emotion + Mem + Group:Mem, random= ~ 1 | Subject, data = dati)
Group is a 2 level factor (Controls, Patients). Given that I have the assumption that the variance of the response variable is different between Controls and Patients I decided to insert a
weight term for the variance
weights = varIdent(form = ~1 | Group):
fit_var <- lme(Score ~ Group + Emotion + Mem + Group:Mem, random = ~ 1 | Subject, data = dati, weights = varIdent(form = ~ 1 | Group))
My question is: if I compare these two models and the
fit_var model is better (in terms of AIC, BIC, LRT) it's like testing also the variance component? In other words, can I say that allowing for the heteroscedasticity in the model, the response variable explanation is improved?