estimated parameters: 0

The first error you're seeing is saying that the fit function for the top-level model returned a value of NaN (Not a Number). This probably happened on its first evaluation, at the start values, since CSOLNP and NPSOL can both deal with NaN once optimization has gotten underway. But, if it happens at the very start, they don't know what to do next, and terminate prematurely.

It's a bit peculiar that you got the warning about "Mx status RED," but you can ignore it for now, since the real problem is that your model won't run.

The last error you got is just R saying it cannot find an object called SatFit. This is because SatFit was never created; the line that would have created it, SatFit<- mxRun(SatModel, intervals=F), led to an error.

You should change the start values for your CorMZM and CorMZF, because those matrices are not positive definite at the values you're currently using. You can see this with, for example, eigen(CorMZM$values)$values, which should all be positive, but that's not the case. You could try lowering the biggest off-diagonal values. Also, it looks like you're starting all of your thresholds at positive numbers. That might be suitable for your data, but it might also be something you'd want to adjust.

BTW, are you running this on a Windows machine?

DeJa Vu, we discussed this this morning :-) This might be a good place to collect that knowledge.

From Mike "the matrix" Neale: "This message is usually caused by parameter values that lead to a likelihood of zero. A non-positive definite covariance matrix (check eigenvalues of the expected covariance matrix) or data vectors that are far from the mean, in standardized units. It may help to bound parameters, or to improve the starting values."

You can sometimes get a model to mxRun with unsafe = TRUE, that can either let it find a good solution - perhaps after another couple of runs - or at least give you something more to work with.

# Explore values of the matrices and algebras driving the expectation
m1 = mxRun(m1 unsafe = T) # unsafe on to get you going
m1 = mxRun(m1) # second run, unsafe off
summary(m1)

omxSetParameters is useful for moving start values, and free/fixing parameters. For instance

m1 = omxSetParameters(m1, "a_r2c1", free=T, values = 0); # drop this parameter to zero
m1 = omxSetParameters(m1, "a[2,1]", free=T, values = .3); # bracket notation if cells are not labeled
m1 = mxRun(m1, unsafe=T)

Setting checkpointing can generate a file of free-parameter estimates that might be useful if you want to see what happened before the condition arose.

 mxOption(factorModel, "Checkpoint Directory", getwd())
    mxOption(model, "Always Checkpoint", "Yes")
    mxOption(model, "Checkpoint Count" , 1)
    mxOption(model, "Checkpoint Units" , "evaluations")   

For the future, having OpenMx should give a message at least this helpful, hopefully taking into account whether this problem happened on the first iteration to help shape this error message.