OpenSEM Forums

I am studying the heritability of a variable by utilizing the ACE model, and would like to ask the OpenMX community if i am analyzing my results correctly.

When i run the ACE model, followed by the AE and CE models, i get the following results for 2 different datasets:

COMPARE ALL MODELS: Print Comparative Fit Statistics
(Nested.fit <- rbind(
mxCompare(SatFit, AceFit),
mxCompare(AceFit, AEFit)[2,],
mxCompare(AceFit, CEFit)[2,]))

Dataset 1

Model A (95% CI) C (95% CI) E (95% CI) (-2LL) df AIC ∆-2LL ∆df p-value

Hi,

I ran the attached ordinal ACE script with age and sex as covariates, and I noticed that for some of my variables I would get the following error message:

"Error in runHelper(model, frontendStart, intervals, silent, suppressWarnings, :
SLSQP: Failed due to singular matrix E or C in LSQ subproblem or rank-deficient equality constraint subproblem or positive directional derivative in line search "

Hello,

I am relatively new to OpenMx. I am wondering if it's possible to compute a profile likelihood CI using OpenMx. I saw the documentation for mxCI(), but I would appreciate if one can provide OpenMx code to estimate a profile Likelihood CI for mediated effect in single mediator model with all observed variable.

I also saw Pek, J. & Wu, H. (2015) article, but it appears that mxCI() can implement their approach, if I am correct.

Thanks for your time in advance,

Davood

Hi all,

Does anybody know a reference in which it is shown that the likelihood ratio test statistic converges to a Chi squared distribution for extended SEMs with definition variables?

With extended SEM I mean RAM models where the entries of A and S may be arbitrary algebras. In contrast to classical SEM where the entries of A,S are either fixed or a single parameter. With definition variables I mean that any fixed value in an SEM may be replaced by a person specific value such that it is different for every person.

I'm doing a bivariate Cholesky decomposition between two variables that were measured at two different time points. I know that it's normally best practice to adjust variables for age and sex effects, and I see many different threads on this board that have discussed how to add covariates to an ACE model. Two questions:

1. For my purposes, should I regress out sex and age at time point 1 for the first variable and then regress out sex and age at time point 2 for the second variable?

Hi! I am a novice, trying to move from univariate to multivariate analysis. So I downloaded the script cholesky.R from Sarah Medland's webpage (https://www.genepi.qimr.edu.au/staff/sarahMe/Workshop13.html), along with the data file. I tried to adapt the script to my data but I ran into an error. So I ran the script as I downloaded it, on the supplied simulated data, without modifying it (see eattachment) and I still encounter the same error, at this point.