OpenMx Structural Equation Modeling

Hi,

I have been able to obtain standard errors for my path estimates by simply using a statement such as:
ACEmodelSumm <- summary(ACEmodelFit)

However, I would like to obtain standard errors for additional parameters, such as genetic correlations and standardized A,C,E estimates (or even simple phenotypic correlations).

Thank you,
Jarrod

(apologies if this has been covered elsewhere)

Hello all,

I am trying to get the correct standard errors for standardized regression coefficients. According to Bentler and Lee (1983) this can be done using nonlinear constraints. I have tried two different model specifications with nonlinear constraints, both of which yield the correct parameter estimates, but neither give correct standard errors (they are returning zeros). Any help would be appreciated. My R code and output are below.

Thanks,

Jeff

Hi all,

I'm new to SEM and OpenMx, but I've been working on replicating models from a paper in which my advisor was involved. I've managed to do so successfully with most of the models in the paper, but two are puzzling me. They are the only two involving endogenous latent variables (the others being measurement models), so I assume this has something to do with it.

I am running a trivariate Cholesky decomposition using a script from the workshop. This script did not include CIs but I included it from another source. However, while the CI script allows me to run CIs for the genetic and environmental correlations, I can't figure out how to modify the script so I can run CIs for my standardized path estimates. The portion of my script that runs the CIs for correlations is below:

corComp_A <- mxAlgebra(solve(sqrt(ACE.I*ACE.A))%*%ACE.A%*% solve(sqrt(ACE.I*ACE.A)), name="corComp_A")

Hi All,

Is there already a helper function to allow users to easily extract the model-implied covariance matrix for the latent variables in a RAM-type model? If not, then I created one (attached). If there was already one, then I've duplicated it under possibly different implementation.

If this is useful to many people, then it could be incorporated into OpenMx at some point.

Hi there,

I am new to OpenMx and trying to fit a factor model. The main part of the script used is shown below:

> manifests <-names(hetcor_T)
> latents <-c("F1","F2","F3","F4")
> twofactorModel<-mxModel(name = "Four Factor Model Path Specification",type="RAM",manifestVars = manifests, latentVars = manifests,
+ mxData(observed=cov(hetcor_T),type="cov",numObs=709),
+ # residual variances
+ mxPath(from= manifests, arrows=2), # latent variances and covariance
+ mxPath(from= latents, arrows=2), # factor loadings for business performance measure variables
+ mxPath(
+ from=c("F1"),

Hi
I am teaching tomorrow and I have a werid glich in my code that I can't resolve
I am modeling the correlations between 3 variables in a very straight forward script. I then want to drop one of the correlations. the difference in fits between the full model and the reduced model is huge and the correlation is not being set to zero
Where have I gone wrong - I can't spot it typo(?)
thanks
Sarah

data(twinData)
selVars <- c('wt1','ht1','bmi1')
nv <- 3
factorData <- subset(twinData, zyg<=5, selVars)

SatModel <- mxModel("saturated",
mxModel("Cor",

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'm learning SEM with the OpenMx documentation. First of all thanks for this I would agree your effort and attention.

I'm a beginner in SEM and I'm not sure about this question. I see a possible bug in documentation http://openmx.psyc.virginia.edu/docs/OpenMx/latest/Regression_Path.html. The specification of a multiple regression and multivariate regression talk about the covariance between x and z in a y equation (resp. z equation). This relation is specified in scripts like:
# covariance of x and z
mxPath(
from="x",
to="y",
arrows=2,

I have a basic question about running models in OpenMx. I am interested in running a trivariate cholesky using two categorical variables (i.e., the presence or absence of a diagnosis) and one continuous variable (personality). Is this possible? If so, do i need to change my basic trivariate script (which I have tested using all continuous variables and it runs) in some way?

Thank you!