OpenSEM Forums

Hi, forgive me if you see duplicate messages. I previously posted the question on my last thread. But I think it's more proper to make it as a new topic.

I’m a PhD student in the field of economics very new using SEM and MX software.

I’d appreciate your feedback with regard to the following basic questions.

Let’s construct a SEM model where 3 measurable variables: X, Y and Z interact with a latent variable G and assume that the factor loadings of latent variable G over the measurable variables X, Y and Z are 0.3, 0.4 and 0.1 respectively. The model is constructed sourced on the correlation matrix (3x3) of the measurable variables.

Then, as far as factor loadings are considered as regression coefficients:

I'm running a simple ACE model of a continuous variable in openMX. I'm running both a constrained and sex-limited model to see which provides a better fit, and then running various constrained or sex-limited submodels where a or c are dropped.

Here is what I have noticed -- in a sex-limited submodel where c is dropped, the a and e variance components and confidence intervals are extremely similar across males and females -- AND, extremely similar to the components for a constrained model where c is dropped.

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?

Does anyone know have a script for running a sex-limited bivariate cholesky within same sex twin pairs?

Hello,
I have ordinal data where the probability for membership in each category is known (e.g. individual i has probability p for being member of category 1 of variable u). Does anybody know if it is possible to utilize this information in OpenMx, for example by weighting the thresholds? The probabilities would be different across twins and across thresholds of the ordinal.

I have just done the bivariate genetic model and I'm not sure about the accuracy of the results.
1. If Rg=-0.76, does this mean the genetic correlation between two traits is negative?
2. is there any relationship between the positive/negative of phenotypic correlation with that of Rg, Rc, or Re? If the phenotypic correlation between two traits is negative, are the Rg, Rc, and Re negative or any of the three can be positive?

Thank you

I'm trying to get chi-square fit index of my univariate ACE model. I tried to use mxRefModels(ACEFit) and then use mxCompare between the mxRefModels output and my ACE model (ACEFit), but it returned NA for the p value

base comparison ep minus2LL df AIC diffLL diffdf p
Saturated twinACE 0 NA 0 NA NA NA NA
Saturated twinACE twinACE 6 879.7042 178 523.7042 NA 178 NA
Independence twinACE 0 NA 0 NA NA NA NA
Independence twinACE twinACE 6 879.7042 178 523.7042 NA 178 NA

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

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