OpenMx Structural Equation Modeling
yoosoo
Joined: 06/19/2014
Multilevel SEM with complex sampling
Dear all,
Would you confirm if OpenMx supports multilevel SEM on complex sampling survey data, with categorical outcome? (each datapoint has different weight)
Thank you!
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CharlesD
Joined: 04/30/2013
Returned matrices (expCov, expMean, A,S, etc) only reflect last row of data
Hi folks. I mentioned this to Joshua who asked me to post it here for further input. While wondering about some strange output in one of my functions the other day I discovered that the matrices output by openmx only reflect the last calculated row of data. I believe this only becomes evident when definition variables are used, such that the A S or M matrices (when using RAM format, though I don't believe it's limited to this case) depend on the definition variable.
brandmaier
Joined: 02/04/2010
Standardized estimates under equality constraints
I was wondering how OpenMx treats standardized estimates under equality constraints.
I ran a linear latent growth curve model in OpenMx and constrained all residual variances to be equal. Not surprisingly, they all get to be the same unstandardized estimate. However, OpenMx reports only a single standardized estimate for the residual, which happens to be the one associated with the first observation. Even if the unstandardized estimates are constrained to be equal, the unstandardized ones need not be and in reality seldom are the same.
[code]
Julia
Joined: 03/29/2012
Adding new algebras in submodels and constraining them
Hi.
I am running a bivariate moderation model and would like to set some constraints in order to test for some nonlinear effects. I am struggling though with adding new algebras into submodels (with subsequent equating them). Let's say that I specify
pathAm <- mxMatrix(name = "am", type = "Lower", nrow = nv, ncol = nv, free=T, labels = c("amM","amC","amU"), values=pathVal)
pathCm <- mxMatrix(name = "cm", type = "Lower", nrow = nv, ncol = nv, free=T, labels = c("cmM","cmC","cmU"), values=pathVal)
krzysiek
Joined: 09/05/2013
lind0r
Joined: 12/27/2013
Unable to reproduce MASEM results from a published study
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.
mari
Joined: 09/26/2013
Does OpenMx support SEM analysis using ordinal/binary indicators and sampling weights
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.
krzysiek
Joined: 09/05/2013
Correlated residuals of manifest variables
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
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CharlesD
Joined: 04/30/2013
Constraining path loading to values of a latent
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!
rabil
Joined: 01/14/2010
Definition Variables in Constraints and Confidence Intervals
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