OpenMx 2.0 Discussion

Posted on
Picture of user. dtofighi Joined: 10/12/2009

covariance matrix of coefficient estimates

Hello,

I am wondering how to get covariance matrix of parameter estimates (e.g., inverse of Fisher's information matrix) in an SEM with OpenMx. I have included a sample code for a path model below. For example, I would like to get covariances between estimates of b1, b2, and cp that include in the covariance matrix.

Posted on
Picture of user. PaulTwin Joined: 01/29/2017
Posted on
Picture of user. tbates Joined: 07/31/2009

Is it possible to mix definition variables and constants in an mxMatrix?

I'm adding covariates to the means of a model.

The value of each covariate is read from the data as a definition variable ( "data.cov1", "data.cov2" etc) in a matrix named "defCovs"

As the betas are all in one matrix, I'd like to also have an "intercept" column, fixed at 1 in the the defCovs matrix to simplify the arithmetic.

Question: Is it legal to include fixed values along with definition variables in an mxMatrix?

so:

Posted on
Picture of user. dtofighi Joined: 10/12/2009
Posted on
No user picture. EWilliams Joined: 03/08/2016

Optimizer + confidence intervals

Version:
OpenMx version: 2.5.2 [GIT v2.5.2]
R version: R version 3.1.2 (2014-10-31)
Platform: i386-w64-mingw32
Default optimiser: NPSOL

I have been running some bivariate twin models without any problems with the NPSOL optimizer. After installing the latest OpenMx version, however, I keep getting the error : "Error in runHelper(model, frontendStart, intervals, silent, suppressWarnings, : NPSOL is not available in this build. See ?omxGetNPSOL() to download this optimizer".

Posted on
Picture of user. bwiernik Joined: 01/30/2014

Bug with summary.MxModel in saved workspace

I'm encountering an annoying bug with the summary.MxModel method.

When I open a saved R workspace that have MxModels saved it and load the OpenMx library, the summary() command doesn't work properly. Instead of running the summary.MxModel method, it just runs the generic summary method, giving something like:


Length Class Mode
1 MxRAMModel S4

If I first open R, load OpenMx, then load the saved workspace into the environment, summary() works as expected.

Posted on
No user picture. pjohnson Joined: 09/19/2009

Compiling OpenMX on RedHat 6.2

On an older cluster, I'm running into a compiler error with OpenMX 2.6.9. I compiled 100s of packages with the stock gcc-4.4.7, but OpenMx fails. I hit different error with gcc-4.9.2. And I wonder what you know about it.

4.4.7 does not get very far at all.

I fiddled around with the environment lots of ways, various GCC. This system says it has boost-devel-1.50 installed.

Attached error from GCC-4.9.2 points at boost and Rstan headers.

What do you think?

Posted on
Picture of user. bwiernik Joined: 01/30/2014

Constraining parameters using mxAlgebra bracket notation

I have model specified in RAM notation where'd I'd like to constrain some parameters using mxAlgebra.

Here is the script:

circumplex <- umxRAM("circumplex", data=mxData(mat[vars,vars],type="cov",numObs=N),
umxPath(var = vars, fixedAt=0),
umxPath(var = latentvars, labels=paste("communal",vars,sep="")),
umxPath(latentvars, to=vars, values=.6, labels=paste("scaling",vars,sep="")),

Posted on
No user picture. britt01 Joined: 08/04/2015

Error with 2.6.9 vs 2.5.2

Hello,

I am running an ACE analyses on ordinal variables and I am finding that my script is failing with an error about stack imbalance when I use OpenMx 2.6.9 but not when I use OpenMx 2.5.2 Below is the Error I received, and I have attached a script that recreates the error when using 2.6.9 and finishes without error when using 2.5.2 adapted from Hermine Maes example script (http://ibg.colorado.edu/cdrom2016/maes/UnivariateAnalysis/onea/oneACEoa.R)

Thanks in advance for all of your help!

Britt

Error
Calls: mxTryHardOrdinal -> mxTryHard

Posted on
No user picture. Arin A Joined: 07/04/2016

MetaSEM Analysis /NA

Dear all,

In a research project, we aim at examining a predictive model in different contexts. The model includes 9 predictors (among which 7 are mediators also), a covariate, and a dependent variable. Our model fits the four contexts very well. We would like to conduct a metaSEM which would allow us to summarise the results of our studies.
We have followed the code developed by Cheung (2016), however, our output gives us error variances which are negative, and the confidence intervals and the p-values are NA. Moreover, whenever we try to run a REM, R stops responding.