OpenMx General Help

Hello everyone,

I'm a graduate student currently learning OpenMX, and I am unfortunately still a novice at OpenMX and genetic modeling in general. I've so far successfully run multivariate Cholesky analyses with OpenMX in R studio, and gotten interpretable data.

However, I am now trying to drop parameters on my current Cholesky model, to get a "best fit" reduced model for my data. I am having trouble understanding how I might do this; I understand it would involve fixing certain parameters at zero, but given my code, I am completely at a loss as to how I might go about this.

I just run the demo script of the package and I don't modify the script.

when I run the demo script AlternativeApproaches.R on my computer, the running cpu is around 100% and completed in 10 seconds.
input:
time Rscript AlternativeApproaches.R
output:
real 0m2.431s
user 0m3.752s
sys 0m0.093s

But when I run the script on my company CentOS server, the running cpu is above 500% and completed in more than 15 minutes.
input:
time Rscript AlternativeApproaches.R
output:
real 2m39.438s
user 15m6.753s
sys 0m2.392s

Dear all, Please help me fixing this problem.

The data set has (please see example diagram)
+ 3 continuous variables(X1, X2, X3) for a latent variable (intercept)
+ 3 mediators (one binary X8 and two categorical variables X6, X7)
+ 1 dependent variable (X4).

This coding was based on the post about Categorical Data in both independent and dependent variables http://openmx.psyc.virginia.edu/thread/3883

When I run SEM.R model I have following error message.

Error in mxRAMObjective(A = "A", S = "S", M = "M", thresholds = "Threshold") :

I am using the Kalman filter implementation (mxExpectationStateSpace) to fit a time-discrete vector auto-regressive model to single subjects' multivariate time series. If I'm not mistaken, the Kalman filter generates (predicted and updated) process state estimates for each but the first time point. Is it possible to pull these state estimates out online while the data are being filtered?

I can see several twin data sets that come with OpenMx, for example, the DZO data described by following page:

http://openmx.psyc.virginia.edu/docs/OpenMx/latest/_static/Rdoc/dzoData.html

Would anyone please provide more background about this data? The sample size seems to indicate that it is not simulated data, but no descriptions are given for the variables.

I would appreciate it. Thanks.

Am I doing something wrong? I was surprised when my model seemed to be ignoring my definition variables, but then I also notice that mxEval interprets them all as NA... this is using a build from yesterday, 1st Feb 2015, on 64 bit windows.

data(demoOneFactor)
nvar <- ncol(demoOneFactor)
varnames <- colnames(demoOneFactor)
demoOneFactorInputs <- cbind(demoOneFactor, V1=rnorm(nrow(demoOneFactor)))
ssModel <- mxModel(model="State Space Inputs Manual Example",
mxMatrix("Full", 1, 1, TRUE, .3, name="A"),
mxMatrix("Full", 1, 1, TRUE, values=1, name="B"),

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)