OpenMx General Help

Hi all,

I install the OpenMx for windows today. I found that I cannot run several example scripts:

http://openmx.psyc.virginia.edu/svn/trunk/demo/BivariateSaturated_MatrixRawCholesky.R
http://openmx.psyc.virginia.edu/svn/trunk/demo/BivariateHeterogeneity_PathRaw.R

The error messages are

Error in mxModel("bivSat6", mxMatrix(type = "Full", nrow = 2, ncol = 2, :
could not find function "mxFitFunctionML"

Error in mxModel("bivariate Heterogeneity Path Specification", mxModel("group1", :
could not find function "mxFitFunctionAlgebra"

Hi fellow community members,

I'm working with quite large data sets ~500000 lines of 4-28 variables (number of covariate patterns >500). I'm trying to simplify the input data into OpenMx by aggregating covariate patterns so that I can specify one line per unique pattern, and then multiply the objective function by how many observations there are of the specific pattern. The reason for me to do this is that I want to save computing time.

I am writing some functions that need to select some submatrices with variable indices. Let's consider the following sample code. The issue is that mxModel() only recognizes x in the gobal environment rather than inside a function. Have I missed something? Thanks.

myFun <- function() {
x <- 2
cat("Matrix in R:\n")
A1 <- matrix(1:16, ncol=4, nrow=4)
B1 <- A1[1:x,1:x, drop=FALSE]
print(B1)
myModel <- mxModel("test",
A2 <- mxMatrix("Full", nrow=4, ncol=4, values=1:16, name="A2"),

Hi all,
Fitting tri-variate models with CIs but CIs look odd.
They are to close to the point estimate and lower point is sometimes higher that point estimate.
Below a selection

MZMmodel.sdAm[1,1] 3.224423e-01 0.32244499 0.39122315
MZMmodel.sdAm[1,2] 4.477139e-01 0.58554106 0.58555366
MZMmodel.sdAm[1,3] 8.285475e-01 0.82850806 0.82855803
MZMmodel.sdAm[2,1] 4.477139e-01 0.58554106 0.58555366
MZMmodel.sdAm[2,2] 4.104425e-01 0.41044353 0.41043733
MZMmodel.sdAm[2,3] 4.554080e-01 0.45540625 0.61216648
MZMmodel.sdAm[3,1] 8.285475e-01 0.82850806 0.82855803

Hi,

I've calculated genetic and environmental cross-trait correlations in a bivariate model, based on the correlational approach for scalar and non-scalar sex limitations (Neale et al., 2006, Twin Research and Human Genetics).

I've tested their significance by looking at the CIs and by checking what happens when I drop each parameter.

Now I have the situation where zero lies within the CIs - but when I drop the parameter, the model changes significantly. How is that possible (--> output attached!)?
(For a different phenotype, this didn't happen.)

I have a complex nonrecursive path model (i.e., only latent disturbance terms for endogenous manifests). It has several reciprocal paths and potential feedback loops. The model converges fine with no errors and the fit statistics all look great (X2 = 9.2, P = 0.24, CFI = 0.99, RMSEA = 0.033) and all the correlation residuals are less than 0.10. However, one of the disturbance estimates is slightly higher than the manifest variables actual variance (d = 1.699, s2 = 1.605), which creates a slightly negative R2 value for that endogenous variable.