OpenMx Structural Equation Modeling

Dear users,

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.

Hi,

I'd like to know if it's possible to estimate a sem model with ordinal and binary indicators using a WLS estimator based on the raw data including the sampling weights. The data set contains a weight column with an individual weight for each case and I don’t need multilevel modeling.

I am currently using the R package lavaan for SEM analysis. As lavaan does not support this functionality, I’m considering changing to OpenMx.

Hi,

Is anybody knows how to model correlated residuals of some manifest variables in OpenMx? Can you give me any example of mxPath()
application to this issue?

best regards,
Krzysztof

I would like to constrain the loading between two variables to the values of a 3rd, where that 3rd is generated by additional structures. I guess I need a way to reference the estimated latent directly, but as it stands I seem to be only able to reference it's parameters.

I've been mainly generating the model with mxPath rather than mxMatrix, but this can change as needed...

Cheers for any suggestions!

I'm trying to build a simple auto-lagged model for some simulated data. The observations are simple ar(1) with 6 observations per subject and where the time points are irregular within and between subjecs. Eid in the Daily Life handbook (pp. 398-402) shows how to do this in MPlus.

The model works fine with equally spaced time points - I can get back the ar(1) value used in the simulation.

To handle unequal spacing, I'm using definition variables. The trick is to constrain the ar loadings. Suppose you have (a simple path diagram):

x1 --> x2 --> x3 --> x4 --> x5 --> x6

I would like to seek for your help for the code. I would like to model a two-factor Cholesky decomposition using 1 continuous and 1 binary variable in the model. Your help is highly appreciated!

Sorry if this is a stupid question, but I have been having trouble getting to grips with OpenMx.

I have been using the sample script UnivariateTwinAnalysis_MatrixRawConACE.R. I would like to extend this in two ways:
* Print out a, c, and e (where appropriate) path variables and the standardized version of these for all models, (i.e. AE, CE, E not just ACE)
* Print out confidence intervals for ACE.A/ACE.V etc.

Can anybody help with this?

Thankyou

Karin

Hi everyone,

I have a problem running multilevel model using a SEM framework to get a likelihood-based CI of the effect size. I have two factors representing (random) intercept and (fixed) slope. I attach my data file here. Use the following code to get the dataset:

datawide <- read.csv("datawide.csv")

The dataset has been transformed into a wide format. Y is the response variable. X is the upper-level predictor. Z is the lower-level predictor without any centering. My target model is

L1: Y = B_0 + B_1 * Z + r
L2: B_0 = X + u