OpenMx Structural Equation Modeling
rabil
Joined: 01/14/2010
mxRowObjective - Any Examples?
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rabil
Joined: 01/14/2010
Constraining Thresholds
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rabil
Joined: 01/14/2010
Poisson Counts
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rabil
Joined: 01/14/2010
Is it possible to model censored data in OpenMx?
neale
Joined: 07/31/2009
Standardized Estimates
# Now standardize solution
mxEval((solve(vec2diag(sqrt(diag(S)))))%&%S,threeLatentMultipleReg1Out)
mlthreeLatentMultipleReg1Out<-omxRAMtoML(threeLatentMultipleReg1Out)
mxEval(solve(vec2diag(sqrt(diag(solve(I-A)%&%S))))%*%A%*%vec2diag(sqrt(diag(solve(I-A)%&%S))),mlthreeLatentMultipleReg1Out,compute=T)
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tbates
Joined: 07/31/2009
Fake Latents
Snuffling around in MxPPMLR I see
# IN DEVELOPMENT
# Fake Latents
# There are multiple ways to specify the error variance terms. There is the
# usual, direct way of allowing the term in the S matrix to be free, but it
# can also be specified using latent variables.
#
# This segment adjusts the model so that all error variance is specified using
# only the S matrix, without any latent variables
This raised a question for me.
I guess this is designed to do change this:
Into this:
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tbates
Joined: 07/31/2009
AIC etc in multi-group RAM models
Running a 2 group RAM model. Both sub-models return their fit indices in summary(), and the supermodel runs fine, with a summed objective.
However... while the supermodel knows about the submodel's observations etc (as shown in the print out below) it doesn't compute an AIC for the the supermodel
Bug/Missing code?
observed statistics: 156
estimated parameters: 79
degrees of freedom: 77
-2 log likelihood: 46372.74
saturated -2 log likelihood: NA
number of observations: 6000
chi-square: NA
p: NA
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brauer
Joined: 01/28/2012
How to get the correlation residuals with the new version of OpenMx?
I recently downloaded the most recent version of OpenMx. Since then I am no longer able to get the model-implied covariance matrix, and therefore, the correlation residuals. Before, the following script would get me the correlation residuals:
residuals <- cov2cor(covmatrix) - cov2cor(modelfit$objective@expCov)
round(residuals, digits=4)
My data are in "covmatrix", a symmetric covariance matrix. The result of the mxRun is stored in "modelfit")
tbates
Joined: 07/31/2009
RAM estimation from covariance matrix where ns differ per cell
When mxData is a cov or cor matrix, numObs is just one number.
Quite often the cells in a covariance matrix could take advantage of different numbers of observations.
Two questions: has anyone made RAM models with an "numObs" matrix to give a per-cell n? And second, are the assumptions of a covariance-based model violated if all data do not come from complete subjects?
brauer
Joined: 01/28/2012