OpenMx General Help

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No user picture. handeezgia Joined: 03/20/2023
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No user picture. lf-araujo Joined: 11/25/2020

Calculating expressions in model

Hi all,

I rewrote mrdoc to shorten the hundreds line model to the version below,
however in this version some paths are mixed in correlations. For example,
abraas is combining ab, ra, and as. I wanted to make it easier to the user
and provide each path separately, but I am not sure how to do this. I tried to use
mxEval, but I think it is beyond my capabilities.

Posted on
No user picture. lf-araujo Joined: 11/25/2020

Still can't print vignettes (linux)

I remember we discussed this in the past, but I can't remember what was the issue.

In a linux system calling vignettes is not listing available pages. Is this happening to other systems too?

I have here a npsol enabled version in a linux system, see code and output below.


> vignette(package="OpenMx")
no vignettes found
> tools::getVignetteInfo("OpenMx")
Package Dir Topic File Title R PDF

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No user picture. thughe01 Joined: 10/18/2009

Forum search issue

Hi there. When I try to use the simple or advanced search in the Forums with more than a single word, I get the following error:

Forbidden
You don't have permission to access /search/node/test search on this server.

Not sure what I am doing wrong? Have tried multiple browsers, clearing cache, etc.

Cheers,

Toby.

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No user picture. lyra9826 Joined: 10/20/2021

Issues with confidence interval estimation for Cholesky Decomposition ACE model

I am estimating a Cholesky decomposition ACE model and deriving the rA, rC, and rE estimates. The confidence intervals are not estimating for many of these parameters. There appear to be varying diagnoses when I use verbose=T (i.e., active box constraint, alpha level not reached infeasible non-linear constraint). I have tried choosing a different optimizer, re-scaling the phenotypes, using mxTryHard, and changing start values. The univariate variance components as well as their correlations are significant.
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Picture of user. rabil Joined: 01/14/2010

Residual Variance as a Function of a Latent Variable

Imagine you have a simple one-factor measurement error model - just one latent variable u with say 5 indicators. How would you make the residual variance of the indicators a function of u? For example, a linear function of u. Is this possible using OpenMx?
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Picture of user. rabil Joined: 01/14/2010

Constraint Not Honored

I have a simple two-factor measurement error model. There is a constraint on the variance of one of the latent factors that it be positive. It easily finds a solution using mxTryHard but the variance estimate is always negative. What is the point of using a constraint if it is not honored? The negative estimate then causes problems when I try to bootstrap confidence intervals. It complains about quantile missing values and NaNs and won't produce a result unless I remove the variance from the confidence interval list.
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No user picture. cjvanlissa Joined: 04/10/2019

MxModel returns class MxRAMModel instead of MxModel?

I'm calling MxModel via do.call, with a list of arguments. Previously, this consistently returned objects of class MxModel. Now (probably after the latest OpenMx update), it returns MxRAMModel, which is inconvenient because not all exported methods work with MxRAMModel.

I'm rolling back my installation of OpenMx now to check if it's due to the latest update, but that takes a while.. in the meantime - does anyone know why I'm getting MxRAMModels and how to prevent that?

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Picture of user. jmatosv Joined: 04/25/2015

Model identified at solution but not identified at start values

Openmx follows an iterative method when executing mxCheckIdentification function. Therefore, on first iteration start values are used to solve model equation; then, the iterative process continues until the best (optimal) solution is obtained.

If we run mxCheckIdentification(pathrun) and the model is locally identified at solution, but we run mxCheckIdentification(pathmod) and the model is not locally identified at start values, would that be a reason of concern when evaluating the model?