Fit Functions
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Chi-Square Confidence Interval
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Dear all,
I used the following line to obtain a confidence interval around the chi-square, but I received an error that this key is not found in the mxOptions.
model1a = mxOption(model1,"Chi-Square Confidence Intervals", "Yes")
Indeed, when I ask for "getOption('mxOptions')" the option does not seem to exist. Is this true? Or am I doing something wrong?
Thanks in advance!
Suzanne
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individual likelihood statistics (classic Mx MX%P)
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Is there some way to export the likelihood function for a vector of observations equivalent to the 1st column of the classic Mx MX%P= command?
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gxe in the presence of rge unrestricted baseline model
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Hello -
I'm using a gxe in the presence of rge model and was wondering if I need to first run an unretricted baseline model to determine the minimized value of -2lnL of the data. If so, does anyone have a script for this?
I'm using a gxe in the presence of rge model and was wondering if I need to first run an unretricted baseline model to determine the minimized value of -2lnL of the data. If so, does anyone have a script for this?
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SRMR with missing data
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I'm trying to create a function to calculate SRMR, but I'm not sure how to do this when there are missing data. Does anyone have suggestions about what observed covariance matrix to use when there are missing data (or does anyone know what mplus or amos use to get SRMR with missing data)?
Also (and I'm sure this is much easier), how can I get the predicted covariance matrix among observed variables in OpenMx? I'm sure this is obvious, but I couldn't figure out how to pull this out of the output.
Thanks!
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Comparing -2LL for ML and FIML models
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A few months back, I spent a few days banging my head against a wall trying to figure out how to relate the -2LL from a covariance model to that of a FIML model so we could make FIML models with no missing data and no definition variables go much faster. The two -2LLs differed by almost exactly 2*pi*n*nvar (the likelihood of the data, as we termed it), but not exactly, and I could never make the right correction.
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potential optimization problem
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There MAY be a problem with the optimization using some likelihood functions in OpenMx. Specifically, the gradient and the standard errors from the output slots in an MxModel are congruent with minimizing -log(L). The calculatedHessian, however, is congruent with minimizing -2log(L). The attached R code provides an illustration.
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A function to return fit indices
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This is something that would be really useful if someone who is actually competent in the area would fix it. I did make a start, and I know that some of this is wrong; some is probably not sufficiently general; and some does indeed match the sem output exactly. So it is a start. Someone, please fix it and include with OpenMx, this would be very useful for a whole bunch of ignoramuses like me who occasionally need to run an SEM or path analysis and aren't covered by the sem package (e.g., need multi-group models).
# starter R code to compute the usual assortment of fit indices
#
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Proportion of variance
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Hi,
I have performed a modification sreach and did all I could do however the fit statistics are really bad -
I have performed a modification sreach and did all I could do however the fit statistics are really bad -
I have a proportion of variance of 92% strangely enough. What does this mean?
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