Hi!
I have run a linear mixed model with repeated measures, but have some trouble with heteroscedasticity. The code and the stdres vs fitted values plot looks like this:
and the fitted vs observed plot looked like:
I feel I have tried almost everything to reduce heteroscedasticity, but nothing seems to help much. However, when including a frequency weight in the model:
the stdres vs fitted plot became:
and the fitted vs observed plot looked like:
which seems to indicate a much bettwe fit than the fitted vs observed plot above.
However, I have never used frequency weights before, and I am not sure whether it is appropriate to use it here (I used it since the earlier time points contain much more observations than the later ones).
Any comments would be greatly appreciated!
Kjell Weyde
I have run a linear mixed model with repeated measures, but have some trouble with heteroscedasticity. The code and the stdres vs fitted values plot looks like this:
Code:
mixed depvar c.agesp*##i.gender cov1 cov2 cov3.......... || ID: age, mle cov(un) residuals(exp, t(time))
and the fitted vs observed plot looked like:
I feel I have tried almost everything to reduce heteroscedasticity, but nothing seems to help much. However, when including a frequency weight in the model:
Code:
mixed depvar c.agesp*##i.gender cov1 cov2 cov3.......... [fw=time] || ID: age, mle cov(un)
and the fitted vs observed plot looked like:
which seems to indicate a much bettwe fit than the fitted vs observed plot above.
However, I have never used frequency weights before, and I am not sure whether it is appropriate to use it here (I used it since the earlier time points contain much more observations than the later ones).
Any comments would be greatly appreciated!
Kjell Weyde