# multivariate ordinal example and question

3 posts / 0 new
Offline
Joined: 07/31/2009 - 14:25
multivariate ordinal example and question

hi,
Trying to get a multivariate ordinal ACE script going with different numbers of thresholds in each variable.

The script is running now, and attached below (self contained: pulls data from a url).

it gives the error

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)

I am not sure what we are supposed to be doing in threshold matrices with cells that are not expected to be used: I have just left them determined to be greater than the preceeding thresholds, and hoping htey don't mind not being used. But are they supposed to be set NA?

Offline
Joined: 07/31/2009 - 14:25
attachment was not attached

attachment was not attached :-)

Offline
Joined: 07/31/2009 - 15:14
It doesn't make code Red go

It doesn't make code Red go away (though it might have) but I notice Full matrices where Lower was expected, so seem to be able to use:

mxMatrix("Lower", nVar, nVar, free=T, values=rnorm(nVar*(nVar+1)/2, mean = .6, sd = .1), name="a"),
mxMatrix("Lower", nVar, nVar, free=T, values=rnorm(nVar*(nVar+1)/2, mean = .6, sd = .1), name="c"),
mxMatrix("Lower", nVar, nVar, free=T, values=rnorm(nVar*(nVar+1)/2, mean = .6, sd = .1), name="e"),


Running several times yields almost exactly the same fit and same error. However, dialing down precision helps:

> est1<-ACEOrdFit@output$estimate > ACEOrdModel <- mxOption(ACEOrdModel, "Function Precision", 1e-8) > ACEOrdFit = mxRun(ACEOrdModel, intervals=F) Running ACE > summary(ACEOrdFit) And does not seem to change the solution a whole lot: > ACEOrdFit@output$estimate

-0.3839011110 -0.0926679446 0.1203437405 0.5555043707 0.5160617608 -0.1834054992 0.8827464781 0.8112786187

0.8133510994 -0.0638410734 0.0099016854 0.0020701259 -0.2708858073 -0.0022325128 -0.0431975228 0.1434395464

-0.0059582264 -0.1483921050 0.9737269653 0.5000000000 0.5000000000 0.5000000000 0.0831593193 0.6315923555

0.5000000000 0.5000000000 0.0001055547 0.4444466554 0.3557393401 0.5369744499
> est1<-ACEOrdFit@output$estimate > ACEOrdModel <- mxOption(ACEOrdModel, "Function Precision", 1e-9) > ACEOrdFit = mxRun(ACEOrdModel, intervals=F) Running ACE Warning message: In model 'ACE' NPSOL returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED) > ACEOrdFit@output$estimate-est1

1.719295e-02 6.996527e-03 3.251360e-02 -9.884858e-04 5.319398e-03 2.996002e-01 6.320578e-03 -1.730106e-03

3.323140e-03 1.595764e-01 -3.278419e-02 -3.226108e-03 -3.130417e-03 1.486215e-03 -8.030822e-04 2.227999e-04

-1.344185e-04 6.018336e-04 -9.379388e-04 0.000000e+00 0.000000e+00 0.000000e+00 -3.853347e-05 -3.186464e-05

0.000000e+00 0.000000e+00 -5.554708e-06 2.171607e-04 -3.820562e-05 -6.856539e-04

The .16 looks like a problem, but it's a diagonal element which is invariant to sign so .09 and -.06 are not that far apart.

Two things then:
i) Make lower as above
ii) Try bounding the diagonals of a, c and e to be positive

and a note: We should stop printing Std Errs when there are constraints in the model, until we fix the issue. They're rubbish.