Published on *OpenMx* (http://openmx.psyc.virginia.edu)

By *berlinator*

Created *06/16/2011 - 09:59*

Thu, 06/16/2011 - 09:59 — berlinator [1]

Hi all,

I am trying to fit a model including three simple latent change regressions with covarying predictors and two latent change scores having direct effects onto the third latent change score. Unfortunately, the optimizer ends up with a non-positive sd covariance matrix. Some of the predictors are rather highly correlated (r ~ .8) and I was thinking wether this failure might be due to predictor multicollinearity. However, Mplus finds a solution! I was wondering if there are some default settings making the OpenMx model different from the Mplus model but I didn't figure it out. Setting the covariances to zero makes the two models (OpenMx and Mplus) look completely the same.

Atached please find the code.

Attachment | Size |
---|---|

xMultColPred.R [2] | 2.45 KB |

**Links:**

[1] http://openmx.psyc.virginia.edu/users/berlinator

[2] http://openmx.psyc.virginia.edu/sites/default/files/xMultColPred.R

[3] http://openmx.psyc.virginia.edu/thread/1006

[4] http://openmx.psyc.virginia.edu/thread/938

[5] http://openmx.psyc.virginia.edu/forums/openmx-help/openmx-structural-equation-modeling