running the attached file & data, the model fits satisfactorily, but summary says:
> ACENonScSLCoraSumm <- summary(ACENonScSLCoraFit)
Error in observedStatisticsHelper(model, expectations[[i]], datalist, :
trying to get slot "type" from an object of a basic class ("NULL") with no slots
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
Per this thread, we have different results for joint ordinal and different numbers of threads
http://openmx.psyc.virginia.edu/thread/2363
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")
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?
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
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
User's should be able to specify a path model and have the LISREL expectation to go with it.
We should have a multigroup fit function built in.
Duplicate the R behavior of matrix(1:12, 3, 4) as an OpenMx algebra.