OpenMx Help

Hello All,

I had just done bivariate ACE model for two continuous variables (bivariate correlated factors model). The results turned out to be a little weird as follows:

A1 = 0.25, A2 = 0.09
C1 = 0.12, C2 = 0.08
E1 = 0.62, E2 = 0.82
Rph = -0.24
Ra = -1
Rc = 1
Re = -0.26

We observe Ra is -1, but we personally think it is not reasonable that the two variables are perfectly correlated. Is it possible that there are some problems with the program?

Dear users,

Hi,

I created a model without any free parameters. I'd like to run the model to obtain the likelihood of some data under this model. The model runs fine but summary() returns the error that the comparison "matrix == nameA" is valid only for atomic and list. It seems that summary() is missing a check for empty lists of free parameters. This is the traceback():

8: eval(expr, envir, enclos)
7: eval(e, x, parent.frame())
6: subset.data.frame(ptable, matrix == nameA, select = "row", drop = TRUE)
5: subset(ptable, matrix == nameA, select = "row", drop = TRUE)

Hi. I've been doing some work on a package to overlay openmx and manage continuous time SEM models. Everything is working great but I am quite stuck with what seems to be some sort of memory management issue with openmx. I've spoken with Tim Brick regarding this previously, but thought I'd put it up here for all to see :)

Hi,

I'd like to reproduce the meta-analytic structural equation modeling (MASEM) results from this study:
soonang[dot]com/wp-content/uploads/2011/04/2007-MISQ-Ang1.pdf

I used the correlation matrix (Table 3, p. 559) as input and specified the paths according to Figure 2 (p. 560).
Additionally, I set the number of observations to 701 (p. 558).
The full openMx code is attached.

The openMx output for the parameter estimates fits the values in Figure 2 quite well.
However, the openMX fit statistics are quite different from the ones in the paper.

I've got the following error message when fitting a one factor model:
Error: The observed covariance matrix is not a symmetric matrix

Below is the code. When looking at the cov matrix it looked quite summetrical to me...

Does somebody have suggestions?

Thanks a lot in advance.

obsnames = c("intrthought","recdreams","flashbk","emoreact","physreact","avoidthou","avoidremind","amnesia","lossinter","detach","restrAff","foreFut","SleepDis","Irritab","DifConcentr","Hypervigilance","ExaggStartle")

obslabels = list (obsnames,obsnames)

values = c(1.00,
.70, 1.00,

Hi,

Finally I managed to fit a longitudinal one- factor model but now I received the following warning message:

NPSOL returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED)

As I am a beginner in OpenMX suggestions and any help is very welcome.
Thanks in advance!

############################################################
This is my code:
############################################################

Hi,

I am getting a bit lost with the following error below. I frequently get it when runnning the specified data matrices. Sometimes the error pops up at different points. As I really cannot see any typos etc. I am starting to get quite annoyed about it.

Does somebody has any suggestions?

I would appreciate it very much!
Thanks already.

title = " Factor model Personality traits at 3 timepoints"
> obs = mxData(observed = akkercov, type = 'cov', means = petrmeans, numObs = 290)
> matrixL = mxMatrix(
+ type = 'Full',
+ nrow = 15,
+ ncol = 3,
+ free = (TRUE,FALSE,FALSE,

Hi all,

I want to fit a one fatcor longitudinal model at three measuremet time points based on an observed covariance/correlation matrix from an published article. Unfortunately I get the following error message when running the obs function, see below.

This is the error:
Error: The observed covariance matrix is not a positive-definite matrix

It would be great if somebody could help with some suggestions!

This is the beginning of the code:

## create observed covariance matrix

obsnames = c("extrT1","beneT1","conscT1","emostT1","imagT1",

Hello All,

First of all thank you for your hard work over the years to provide us with unique ways to combine meta-analytic principles with SEM--and providing a conduit to do this in R. My research team and I really appreciate it.

I am doing a two stage MASEM with the metaSEM package, and am having some difficulty specifying a mediation analysis in the A/Smatrix. I have four variables: w,x,y,z. And my linear equations are as follows:

X ~ W + e
Z ~ W + e
Y ~ W + X + Z + e

I would like to see if X or Z (or X and Z) mediate the relationship between W and Y.