I know it's a strange model, but what I'm trying to do is model a regular old unidimensional factor analysis, with the exception that the uniquenesses are all correlated with the factor. Here's how i've tried to specify my model:

man = paste(rep("Item", times=20), seq(1:20), sep="")

errors = paste(rep("e", times=20), seq(1:20), sep="")

lat=c("F1", errors)

loadings=mxPath(from=lat[1], to=man, arrows=1, values=.5, free=T)

fVar = mxPath(from=lat[1], arrows=2, free=F, values=.5, labels="D")

uniq = mxPath(from=lat[2:21], to=man, arrows=1, free=T, values=.5)

tecorr = mxPath(from=lat[2:21], to=lat[1], arrows=1, free=T, values=1)

fa = mxModel(

name="OmegaTest",

type="RAM",

mxData(observed=ddat, type='cov', numObs=20000),

manifestVars = man,

latentVars = lat,

loadings,

fVar,

uniq,

tecorr

)

From this, I get an error message saying: "The job for model 'OmegaTest' exited abnormally with the error message: Backing out of parameter space region where expected covariance is non-positive-definite."

It sounds like a specification problem, but how do I fix it? How do I tell R that I want all the residuals to correlate with the factor? I've included a (condensed) diagram to show you what I'm trying to model. Thanks!