Colleagues -- For examining the shape of repeated responses over time, if time is specified as an ordered factor then can at least the lower orders (linear, quadratic, cubic...) and their interactions that emerge from the mixed model analysis be interpreted directly?
What to make of the differences in AIC, BIC, and random effect variance, from the model that does not declare time as an ordered factor?
How best to think about those degrees of freedom?
Or is there some preferred alternate strategy?
dF <- as.data.frame(cbind(rep(1:50,rep(5,50)), rep(1:2,rep(5,2)), rnorm(1:250), rep(1:5,50)))
names(dF) <- c('respondent', 'group', 'measure', 'time')
m.time_not_ordered <- lme(measure~group*time, random=~1|respondent, data=dF, method='ML')
m.time_ordered <- lme(measure~group*ordered(factor(time)), random=~1|respondent, data=dF, method='ML')
Many thanks - Dave McArthur
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