General SEM Discussions

Hi all,
Is there a recommended approach to modeling twin data in openmx where the variables are semi-continuous/skewed such that they are exhibiting a ceiling effect? Alternative estimation method perhaps, or is ML robust enough to such violations?

Hi All,

Just thought I'd post the headline. I'll post again once I clean up my example script.

I know it's a strange model, but what I'm trying to do is model a regular old unidimensional factor analysis, with the exception that the uniquenesses are all correlated with the factor. Here's how i've tried to specify my model:

man = paste(rep("Item", times=20), seq(1:20), sep="")
errors = paste(rep("e", times=20), seq(1:20), sep="")
lat=c("F1", errors)
loadings=mxPath(from=lat[1], to=man, arrows=1, values=.5, free=T)
fVar = mxPath(from=lat[1], arrows=2, free=F, values=.5, labels="D")
uniq = mxPath(from=lat[2:21], to=man, arrows=1, free=T, values=.5)

Hi everyone,

I have one problem and this from will be helpful.

I am using PLS for analyzing my data, i have created product terms for testing moderating effect. But one of the interaction term (moderating effect) gives me -1.25 path coefficient. Can anybody help where is the problem? I double checked the data all the interaction terms are fine.

Thank you.

Subjects have 5 measures on each eye. DA is an area measurement and the C's are counts of area size categores. The C's can be linearly combined to closely estimate DA. The idea is that DA and the C's are indicators of the true area.