Hi

I tried running a model with vs. without a feedback loop. There is some penalty if there's feedback in the model, but not as much as I would have expected. This is in a system with 48 latent+observed variables, so I would have thought that inverting 2 48x48 matrices every iteration would have slowed things down a lot more. But perhaps the inversion routine is pretty smart about cases like this. Script attached.

> system.time(twinACEFit <- mxRun(twinACEModel))

Running twinACE

user system elapsed

239.818 0.429 240.693

> #summary(twinACEFit)

>

> ##Now add paths for feedback loop

> twinACEModel$MZ@matrices$A@values[1,2]<-.1

> twinACEModel$DZ@matrices$A@values[1,2]<-.1

> twinACEModel$MZ@matrices$A@values[7,8]<-.1

> twinACEModel$DZ@matrices$A@values[7,8]<-.1

> system.time(twinACEFitFeedback <- mxRun(twinACEModel))

Running twinACE

user system elapsed

253.815 0.474 253.921