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 https://openmx.ssri.psu.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.