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mxRename of fitted model objects leaves mxConstraint() objects unrenamed

Submitted by neale on

require(OpenMx)
aMat <- mxMatrix("Full", 1,1, name="aMat")
aCon <- mxConstraint(diag2vec(aMat)==0,name="aCon")

mx101a13 <- mxModel( "mx101a13", aMat, aCon)
summary(mx101a13run <- mxRun(mx101a13))
mx101a15 <- mxRename(mx101a13run, newname="mx101a15")
summary(mx101a15)

Yields:
> summary(mx101a15)
Error: The following error occurred while evaluating the expression 'diag2vec(mx101a13.aMat)' in model 'mx101a15' : object 'mx101a13.aMat' not found

presence of mxCI breaks Standard Error reporting

Submitted by neale on
Adding mxCI() to a model makes all SE's reported as NaN. I don't think it should.

data(demoOneFactor)

Rff <- mxMatrix(type="Stand",nrow=1,ncol=1,free=F,name="Rff")
L <- mxMatrix(type="Full",nrow=5,ncol=1,free=T,values=0.2,labels=paste("l",1:5,sep=""),name="L")
I <- mxMatrix(type="Iden",nrow=5,ncol=5,name="I")
C <- mxAlgebra(L %*% Rff %*% t(L),name="C")
U <- mxAlgebra(I-(I*C),name="U")
SD <- mxMatrix(type="Full",nrow=5,ncol=1,free=T,values=0.6,
labels=c("sd1","sd2","sd3","sd4","sd5"),lbound=0,name="SD")

Add options to control numerical precision of sadmvn integration routine

Submitted by neale on
The file omxSadmvnWrapper.cpp has the following hard-coded precision options for numerical integration:

double absEps = 1e-3;
double relEps = 0;
int MaxPts = 10000*cov->rows

The last of these deviates from classic Mx setting of

1000*ordCov->rows * ordCov->rows * ordCov->rows;

We have been experiencing persistent difficulties with optimization when the number of ordinal variables is large - more frequently than with classic Mx. Possibly the MaxPts setting could be changed to agree with classic Mx?

faulty warning message?

Submitted by Hermine on
Warning message:
The job for model 'ccc' exited abnormally with the error message: Objective function returned a value of NaN at iteration 0.1.

The attached file is an RData file (but saved as .sav to upload)
However, the objective function looks fine in the fitted model object and summary.
estimated parameters: 0 (all parameters were intentially fixed to starting values)
degrees of freedom: 2266
-2 log likelihood: 2885.098


> mxEval(MZf.objective,cccFit)
[,1]
[1,] 754.0471
> mxEval(MZm.objective,cccFit)
[,1]
[1,] 719.298

omxMnor() problem

Submitted by neale on
Funky output with non-positive definite input matrix. May be funky otherwise too. See:

but note that there are some issues with the script, which I have fixed in the attached. Data here also, for convenience.

http://openmx.psyc.virginia.edu/thread/1949#comment-4591

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