By the "spatial power law", perhaps you are referring to the version of 1st-order auto-correlated residuals where differences in timing intervals are taken into account. The Mplus UG example 6.17 shows how Model Constraint can handle the case where the timings have the same intervals. Here the "corr" parameter is the correlation between adjacent residuals. Residuals two points apart will have correlation corr**2, etc. So with non-equidistant time points, you will have to adjust those Model Constraint settings.
If there are individually-varying times of observation I think you could use the VARIABLE option CONSTRAINT = in order to read in individual timing differences and then use that constraint variable (or variables) in Model Constraint.
If you don't have individually-varying times of observation, the easiest way to handle the residual correlations for non-equisdistant time points is to let each pair of adjacent time points have their own residual covariance parameter.
In my discussion here I have assumed the use of the "wide" data approach to growth modeling, the single-level, multivariate approach.
You are right that times between observation are individually-varying, but there is a variable indicating the distance which I could use in the constraint option.
Sorry for being imprecise - I assumed with about 54 repeated measures using ANALYSIS: TYPE = TWOLEVEL RANDOM would be the only applicable way. I have used the "long" data approach, so far, because number (range 44-76, mean 54) and spacing of measurements differs between individuals.
Do you see any objections having 76 variables in the multivariate approach and many missings for some individuals?
I found that is not possible to use the CONSTRAINT command when using TYPE = TWOLEVEL, right? I try to work it out the way you described it and might ask again.
Again, thank you very much for your help. Best, Cornelia
You are right that having as many as 76 variables wide with a lot of missingness is an awkward way to go. The long, two-level approach is better in this case. Model Constraint is available for Type = Twolevel in the current Mplus version. I am not sure, however, how to implement this correlated residual approach in the long approach.
Roy Stewart posted on Friday, January 29, 2010 - 4:40 am
I hope that in the meantime, someone has a good Mplus-example for an unbalanced longitudinal model with non-equidistant time points, where the correlated residuals were implemented in the long approach. Is there anyone who can give me this example or references?