R Functions and User Interface

Hi there,

This should be a quick question considering it is probably common knowledge for all of you. I am looking for some clarification regarding the mxcompare output. Just to be certain, is "diffll" synonymous with chi-squared? From the openmx user guide I cannot see where else a chi-square output would be located. If this is not the case, how can one determine the chi-square value from the usual mxcompare output?

Thank you in advance and please excuse my extreme naivety.

Hey,

I am currently trying to profile the mxRun function on OS X. I am stuck because the fortran code is not at the correct place in the call tree. I described the problem in detail here: http://stackoverflow.com/questions/32378821/profile-compiled-r-code-including-c-and-fortan-on-os-x. Does anybody have any experience with stuff like this?

I know NPSOL will accept first derivatives (gradient) and second derivatives (Hessian) of the fitting function with respect to the parameters.

If I have determined these analytically for my model, can i make OpenMX parse them to NPSOL?

The only slot I can find for the gradient and the hessian are in a fitted model object not in an model hat still has to be fitted.

I did find the mxOption slot where i can tell NPSOL the gradient and hessian will be provided and need not be estimated

Michel

The attached file runs fine under 1.3 but fails unpleasantly under the current svn trunk (2484).

> LCA2 <- LCAfun(data[4:8], 2, 5, 3)
Error in as.character.default() :
no method for coercing this S4 class to a vector
In addition: Warning messages:
1: In mxFIMLObjective(covariance = "R", means = "M", dimnames = nameList[1], :
Objective functions have been deprecated. Please use mxExpectationNormal() and mxFitFunctionML() instead.
2: In mxFIMLObjective(covariance = "R", means = "M", dimnames = tempVar, :

Suppose I have data on n subjects with at most 3 time points. The first subject has x1, x2, and x3 responses at times 0, 1, and 2. The second has time 0, and 3, and the third only at time 0 and so forth. Would it make sense to create 3 definition variables, say d1, d2, and d3:

d1 d2 d3
0 1 2
0 3 NA
0 NA NA

and use data.d1, data.d2, and data.3 for the slope path labels in a growth model that has a latent intercept and a latent slope?

Can OpenMx handle Poisson counts for manifest variables? I didn't notice any examples and a search for Poisson and/or count did not prove helpful. Thanks.

Hey, all.

I put this together for someone and thought I'd share it. It's a quick helper function for building an MxAlgebra object that takes a string instead of an expression as the first argument.


stringToMxAlgebra <- function(algString, name=NA, dimnames=NA) {
eval(substitute(mxAlgebra(tExp, name=name, dimnames=dimnames), list(tExp = parse(text=algString)[[1]])))
}


Hi,

I just found that the omxMnor returns a value for an input involving a non p.d. covariance matrix.

Example

omxMnor(array(1,dim=c(2,2)),cbind(0,0),cbind(-Inf,-Inf),cbind(0,0))

returns a value of .375. The limiting value should be .5

S<-diag(1,2);
S[1,2]<-S[2,1]<-2
omxMnor(S,cbind(0,0),cbind(-Inf,-Inf),cbind(0,0))

returns a value of 0.4262082, though S is not p.d.

Hi! I'm really a new beginner but had a lot of help from the Simplified OpenMx manual. In the end of this manual there is a script that I used to explore my own data. However, this script give 95% CIs for the unsquared path estimates but for standardized variance estimates which would be of great interest. Is there a simple way to modify this script to get 95% CIs also for the variance estimates?? Really grateful for any help!

So I put together this function to estimate polychoric correlations by ML. It may improve on the polychor() by i) accepting a dataframe with more than two variables; ii) avoiding the threshold-estimates-in-the-wrong-order problem (see http://r.789695.n4.nabble.com/polychor-error-td806416.html).

Question is, how should I dispose of this function? Obviously it needs a bit more tidying up - error checking on input, testing, documentation etc. but it could be quite handy. Comments welcome.

> polychoricMatrix(data[1:2])
Running model
$polychorics
t1neur1 t1mddd4l