OpenMx Help

Hi,

I'm a new user and I'm not familiar with programming...I'm trying to install OpenMx 2.0. (building it from the source repository) following the instructions attached in the next link :

http://openmx.psyc.virginia.edu/wiki/howto-build-openmx-source-repository

I'm running a demo script for a one factor model with ordinal data. I'm using OpenMx 1.4-3532 and R version 3.1.2 (2014-10-31). I get the following error message:

> thresholds <- mxThreshold( vars=c("z1","z2","z3"), nThresh=c(1,1,2),
+ free=TRUE, values=c(-1,0,-.5,1.2) )
Error: could not find function "mxThreshold"

Using "library(help=OpenMx)", "mxThreshold" does not appear. What gives?

This occurs after I change the data associated with an otherwise working matrix specification model... I don't think it should :)

Dear all,

I am working on a simplex script in openMx. There are quantitative and qualitative sex differences for my phenotype, so I estimate separate paths for males and females and I would also like to freely estimate the dos correlations between the latent As. However, I am struggling with the transmission paths.
Probably, someone else has already dealt with this. I would like to know whether there are any simplex scripts available that take into account sex differences?

Regards
Charlotte

Hi,

I am trying to detect and identify bivariate outliers in a dataset using OpenMx, in order to see whether specific outliers have significant contribution. Preferrably the output would be like that of %p in old Mx.
(i.e. 8 columns with:
1) -2lnL,
2) Mahalanobis,
3) estimated Z,
4) number of observations in data set,
5) number of data points in vector,
6) optimization details,
7) whether or not likelihood was calculable, and
8) model number if there are multiple models)

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!

I have a data set with six binary variables, which I am trying to determine the temporal relationship. I was using lavaan R package, where they suggested to use dummy variable for endogenous variables (independent) and use ordered for exogenous (dependent variables). I was using the model as described in pdf file. I have gotten following results:

lavaan (0.5-16) converged normally after 31 iterations

Number of observations 51

Estimator DWLS Robust

CSOLNP is working quite nicely in general, but I have a few circumstances where things go dramatically wrong and crash my machine (hard reboot needed) due to excess memory usage if I don't notice fast enough. This doesn't occur when using npsol. With the earlier memory usage issues ( http://openmx.psyc.virginia.edu/thread/2551 ) memory usage increased gradually, but in this case it seems much more sudden.

A problem model can be downloaded from:
https://www.dropbox.com/s/kgpsualdlnaowhs/memprobmodel.RData?dl=0

test <- mxRun(memprobmodel, intervals=T)

Hi all,

I hope I'm just not seeing something, but the way you extract coefficients and fit statistics seems to have changed. It used to be that I could extract the coefficients by typing:

summary(model)$parameters

Any chance of this being implemented at some point? Would seem to be consistent with the omxExponential implementation. Currently I optimize some free parameters over the range 0 to -inf, which is exponentially scaled, and I suspect this probably poses optimization difficulties....