OpenMx Help

Hi I tried the method provided by Horn to solve the problem. However, it does not work. I still receive the error message like:

> require(OpenMx)
Loading required package: OpenMx
Error in dyn.load(file, DLLpath = DLLpath, ...) :
unable to load shared object '/Library/Frameworks/R.framework/Versions/2.13/Resources/library/OpenMx/libs/x86_64/OpenMx.so':
dlopen(/Library/Frameworks/R.framework/Versions/2.13/Resources/library/OpenMx/libs/x86_64/OpenMx.so, 6): no suitable image found. Did find:

Good morning,
I need to know if there will be a twin model fitting course during 2012.
Thank you

There seems to be two issues in using omxSetParameters to reset bounds. Please see below.

rm(list=ls());
require(OpenMx)

A<-mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=.8,label="a",lbound=0,name="A");
model<-mxModel("model",A,mxAlgebra(A^2,name="A2"),mxAlgebraObjective("A2"))

###Now I would like to remove the lower bound

model0<-omxSetParameters(model,"a",lbound=NA)

### This gives an error: " 'lbound' argument must either be NA or a numeric vector. "
### But I was using NA exactly.

model0<-omxSetParameters(model,"a",lbound=NaN)
model0@matrices$A

Can anyone direct me to a script indicating how to run a constraint model? I am combining two twin data sets and want to run a fully constrained and fully unconstrained model in order to know if the two samples can be combined. How can I tweak my multivariate Chlolesky to do this?

Hello,

When analyzing a correlation matrix, do I need to put a constraint to the estimated Sigma so that its diagonal equals identity during optimization? As in Mx? Or has this been taken care of already in openMx?
Thanks in advance!

Suzanne

Hello all,

I am calculating the observed covariance matrix from the correlation matrix and the standard deviations. However, the resulting matrix apparently is not symmetric anymore (although judged by me it is...). Leading to the error message: The observed covariance matrix is not a symmetric matrix.

I hope somebody can help me out, thanks in advance!

Here is the code:

X = matrix(0,8,8)
val = c(1,.39,.35,.21,.32,.40,.39,.39,
1,.67,.11,.27,.29,.32,.29,1,.16,.29,
.28,.30,.37,1,.38,.30,.31,.42, 1,.47,
.42,.58,1,.41,.51,1,.42,1)
X[lower.tri(X, diag=TRUE)] = val

Put your UI- and R-tips here.

I am starting a new thread on this but see
http://openmx.psyc.virginia.edu/thread/831
for background.

I am still attempting to replicate the results from Sorbom's paper using the code posted by Rine and some of the new options to mxRun outlined in the old thread. I am attaching three files.
mi.r contains my code for modification indexes
sorbom.r creates the correlation matrix from one of the examples in Sorbom's paper and runs it
sorbom.txt is the resultant output showing the value of mi I get,

what does the warning "Display list redraw incomplete" mean?

In this case it was followed by
"2: In model 'Full G by E' NPSOL returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED)", which I understand :-)

So I am familiar with being able to refer to an MxModel object, and in particular its objective function, by name, thusly:

algObj <- mxAlgebra(-2*sum(log(classProbs[1,1]%x%Class1.objective + classProbs[2,1]%x%Class2.objective)), name="mixtureObj")