General SEM Discussions

Hi all, I have built a SEM model as attached image. There are five variables, and one modulation factor.

If I know four of the five variables, can I use the model to predict the 5th variable? How can I do this?

This is what i wrote for my model specification but I'm getting this error
2: In eval(expr, envir, enclos) :
Could not compute QR decomposition of Hessian.
Optimization probably did not converge.

> model.dhp <- specifyModel ()
1: EA_response -> EA_survival,NA,1
2: EA_response -> EA_progeny,gam12
3: EA_response -> EA_progeny_wet_mass,gam13
4: EA_response -> EA_progeny_dry_mass,gam14
5: FC_response -> FC_survival,NA,1
6: FC_response -> FC_progeny,gam22
7: BA_response -> Ba_emergence,NA,1
8: BA_response -> Ba_shoot_length,gam32
9: BA_response -> Ba_root_length,gam33

I have a question. I looked at the package sem and lavaan.survey for the factor analysis but sem doesn't support weights and lavaan.survey doesn't have FIML. Without FIML, lavaan.survey was still able to do the job of getting the expected graphs (smooth curves) but with OpenMx, it does not work. I get straight lines.

The code is below and the data set is in the attachment. I don't know if the weights are calculated with the data in the analysis.

dsetA <- read.table("dsetA.txt",sep="")
# Possible values of the weighting variable
valm <- seq(21,40,by=1/10)

Hi all. So I'm taking a look at a dataset of about 200 individuals, each with a number of variables measured 50 times longitudinally. A lot of these variables are scored on a scale of 0-100, which 'seems' to have created large differences in scale usage - differences that are no doubt normally there with a 0-10 scale but are probably emphasised now. If the measurement model were equivalent across individuals, then the structural model is clearly not anyway, so I have a similar issue, or probably both measurement and structural differences.