OpenMx Structural Equation Modeling

Hi,

Thanks to the script below (suggested by Athanassios Protopapas and further developed by Paolo Ghisletta, thank you!!!) I was able to obtain a large number of fit indices, but I still don't know how to get (a) the 90% confidence interval for RMSEA, (b) p close (the test of the null hypothesis that RMSEA (in the population) in less than .05), and (c) the residual correlation matrix. Can anyone help me?

Hello everybody,

I'm slowly converting to OpenMX because of its fiml capabilities. I recently built two models that are shown in the enclosed attachments. You will notice that the correlation between T and P is identical in both models (whether S or D is used). However, when I try to replicate these results in OpenMX, I don't get identical results; they're usually about .04 different, which is quite significant for what I'm doing.

Here's my openMX code:

sem.model.S = mxModel("Two Factor Model Path Specification",
type="RAM",
dd,
manifestVars = names(data),
latentVars = c("T", "P"),

Dear all,

I would like to add a constraint in my EFA model to ensure that Z is diagonal. Z is a function of other matrices. I used an mxConstraint, where J is a conformable identity matrix. My question is, do you see a way of specifying this using labels instead of an mxConstraint?

algebraZ = mxAlgebra(expression = t(L)%*%G%*%solve(F), name = "Z") # should be diag

constraint1 = mxConstraint(Z == J*Z, name = "oblique")

Thanks in advance, I can give my full code if needed.

Suzanne

I am curious if there is a straightforward way to obtain either the fitted covariance/correlation matrix or the residual matrix?

TIA

Does anyone have or know of where I could find examples of models where some paths are functions of other parameters? For example, suppose I wanted to estimate two parameters for three paths so the third was a function of the first two: a, b, b + a/2. I've been looking at the ABO blood group example in the documentation, which does some things similar to what I want, but it is a stretch.

Thanks in advance!

Whenever I try to fit models and add covariances with arrows=2, I always have a heck of a time getting the model past the 'expected covariance is non-positive definite' error. Is this normal? I do not have nearly this much trouble with arrows = 1. Here is a little example using a built in dataset:

#########################
summary(mxRun(m <- mxModel("Example", type = "RAM",
manifestVars = colnames(ability.cov$cov), latentVars = "G",
mxData(ability.cov$cov, type = "cov", numObs = ability.cov$n.obs),
mxPath(from = "G", to = colnames(ability.cov$cov)),