OpenMx Help

Hi, I'm using Onyx, which I think is based on OpenMx, for a confirmatory factor analysis.

I'm using a Macbook Air, and I can't work out how to modify the path arrows so they look neat. Can anyone help?

There is nothing in the documentation. The models in the examples all look nice, whereas everytime I add a path or covariance path, the program sticks them in anywhere and I can't move the end-points around to different parts of the observed or latent variable.

Please help!

Jay.

I created a model (using a function) saved as "x". I would like to change a label in the A matrix. Here are the labels for the A matrix:

> x$matrices$A$labels
v_1 v_2 v_3 mu
v_1 NA NA NA "b_1"
v_2 NA NA NA "b_2"
v_3 NA NA NA "b_3"
mu NA NA NA NA

I would like to change "b_3" to "b_2":

> x$matrices$A$labels[3,4] <- "b_2"
Error: Right-hand side of assignment operator has illegal value FullMatrix, SymmMatrix, FullMatrix, and FullMatrix

Why does this not work?

Could someone point me in the correct direction to download the 1.4 source and then compile and install it on Ubuntu?

I'm trying to install the latest build for OpenMx 1.4. But I get an error:

checking for inst/npsol/linux/x86_64/gcc4.9/libnpsol.a... no
configure: error: npsol library not found

I had 1.3.2 installed. Then I installed 2.2.6, but decided I wanted to use 1.4. The installation attempt is shown below. How can I fix this?

> source('http://openmx.psyc.virginia.edu/getOpenMx1.R')
Installing package into ‘/usr/local/lib/R/site-library’
(as ‘lib’ is unspecified)
trying URL 'http://openmx.psyc.virginia.edu/packages/src/contrib/OpenMx_1.4-3532.tar.gz'

Hello,

I have completed a standard ACE model of a phenotype with no errors. I now want to permute the zygosity labels of the twins and perform 10,000 permutations of the ACE model. I will then use this distribution of permitted A estimates to estimate the significance of my observed A.

Hi,

This is actually quite a quick question and the title says most of it already.
Anyway, is there anyway to constrain mxAlgebra objects across multiple submodels?

I would like to constrain estimated heritability of two separate twin models to be the same while allowing each submodel to have separate parameter estimations.

Unfortunately I was unable to find a way to do so. I have tried mxConstraint(modelA.algebra1==modelA.algebra2) but that seemed not to work.

I would be thankful for any reply!

I am running a CFA and would like to use the "effects coding" method of identification described by Little, Slegers, and Card (2006). In the effects coding method, the loadings of a factor are constrained so that the average of the loadings equals 1 and the average of the intercepts equals 0. How can I do this with OpenMx? I imagine it involves mxConstraint or mxAlgebra, but am not sure where to begin. Here's a small example of a model that I'd like to modify to use effects coding:

oneFactorModel <- mxModel("CFA",
type="RAM",

I am using a bivariate cholesky model for combined continuous and bivariate variables (bw) with Age effects on thresholds.

I am stuck with the error message in running model 'CholAce':A definition variable has been declared in model 'ACE' that does not contain a data set.

The code:
#--------------------------------------------------------------------
CholAceModel <- mxModel("CholAce",
mxModel("ACE",
mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE,values=svPa, labels=labLower("a",nv),
lbound=lbPa, name="a"),

I am a bit confused what the latest stable OpenMx Version is. The last news announcement is for 2.0.1, the install script installs 2.2.4 on my linux supercomputer and fails to install 2.2.2 on my mac laptop (see http://openmx.psyc.virginia.edu/thread/3997).

Are the default confidences intervals for mxCI 95% or 90%? Or is there something funky in the documentation of mxCI? My understanding of a 95% confidence interval is that it is equivalent to a two-tailed test with alpha = .05. That is, it delimits the 2.5% most extreme values at the low end and the 2.5% most extreme values at the high end, leaving the 95% most likely values between the confidence intervals.