Hello,

I am delighted that OpenMx is now available for SEM and I am working my way through the tutorials. However, one of my students has a slightly different two factor model setup to that shown in the tutorial and i am trying to modify the 2 factor example accordingly, but I am getting a bit stuck with the setup ...

In particular, I am trying to modify the two factor model given on this page http://openmx.psyc.virginia.edu/docs/OpenMx/latest/FactorAnalysis_Path.html so that, in terms of the RAM diagram, i do not have a 2 headed arrow to and from F1 to F2, but instead just have a single headed arrow from F1 to F2.

Also I would like to obtain a measure of the covariance (or correlation) of F1 and F2 to show how strongly they are related.

To achieve this, I assume I have to modify this section of mxPath:

# latent variances and covariance

mxPath(

from=c("F1","F2"),

arrows=2,

all=TRUE,

free=TRUE,

values=c(1, .5,.5, 1),

labels=c("varF1","cov","cov","varF2")

to something like:

# latent variances and covariance

mxPath(

from=c("F1"),

to=c("F2"),

arrows=1,

all=TRUE,

free=TRUE,

values=c(1),

labels=c("F1onF2")

however, whilst the model runs fine with this spec i am getting a bit lost with the output. In particular, how can I see how strongly F1 and F2 are related?

Any advice?

Thanks,

Mark.