Dear Forum,
I am trying to fit a longitudinal Cholesky model (two variables in two time-points) with the umxACE and umxModify commands from the umx package.
Looking at the raw correlations and univariate models, it seems like a DE model is the one that fits most of my variables. However, I saw in this discussion: https://openmx.ssri.psu.edu/thread/4201 and in this discussion: https://openmx.ssri.psu.edu/thread/4047 that it is wrong to do a DE model without considering the A of the variables, and that in relatively small sample sizes (as in my case) lack of A may simply mean lack of power to detect A.
My question is- how should I approach the fitting process (i.e., the process of dropping paths to check their significance)? If I drop only one path at a time- none of the paths are significant, and I think it is because each time the model has another path to which it can allocate the variance. But If I drop all, I have a model that doesn't have A in it.
Is there a systematic way to drop paths? Also, should I drop several paths at once, or drop them one by one? As mentioned, the latter option results in a model with no A (a practice that is not advised), and that doesn't fit the raw correlations in terms of heritability estimates.
I attach the raw correlations in case that it will help.
rMZ rDZ
var1_time1 0.29 (0.09) 0.04 (0.05)
var2_time1 0.39 (0.08) 0.17 (0.05)
var1_time2 0.37 (0.1) -0.01 (0.06)
var2_time2 0.38 (0.1) 0.01 (0.06)
Thank you very much,
Lior