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Output codes detail the cause of the error.
This status element is returned from the optimizer (NPSOL).
A value of 0 means a successful optimization--no error returned.
A value of 1 means that an optimal solution was found, but that the sequence of iterates did not converge. There are several reasons this can happen, including starting at the correct values. It generally does not indicate a problem. These estimates can generally be considered correct solutions, so this code is labeled (Mx status GREEN).
A value of −1 means that the optimizer found itself stuck in a location where the objective function could not be calculated, and could not find a way out. This most often happens if the starting values make the calculation impossible.
A value of 2 or 3 means that the bounds or constraints, respectively, could not be satisfied.
A value of 4 means that the iteration limit was reached, with no solution found. You can use mxOptions() to set a higher iteration limit, or just run mxRun() using the output –it will restart from the most recent set of estimates, and make another run of the same number of iterations.
A value of 6 means that optimality conditions could not be reached, and the optimizer could find no way to improve the estimate. It often implies either a mistake in the model specification or starting values in an intractable range. The estimates resulting from this run might NOT be optimal estimates, and it would be risky to treat them as solutions without further investigation, so this code is labeled (Mx status RED). Sometimes re-running the model from its solution [firstRun <- mxRun(myModel) followed by secondRun <- mxRun(firstRun)] is sufficient to make code Red disappear.
Any other status codes should be reported.
output$status[] returns the error code from OpenMx, which is usually −1. This indicates that some aspect of the objective calculation could not be completed at the current location.
The third element of status provides detailed information on why the objective function could not be calculated. Examples are:
Note, while R coders often use the period character inside variable names, this is not legal in OpenMx, which uses this character to specify the container model for variables.
to = c(,'x','y','z')
mxMatrix("Name", nrow = 3,ncol=3,free=TRUE, values=.5,)
mxMatrix(type = "Full", nrow = 1, ncol = 1, free = FALSE, values = "l1", labels = "aa", lbound = 0, ubound = 2, name = "la"),
We try and convert the character "l1" to a numeric value, can't, return an NA... hence the error: NAs induced by coercion.
Warning messages: 1: In convertVFN(values, free, labels, lbound, ubound, nrow, ncol) : NAs introduced by coercion...
Error: The expected means matrix associated with the FIML objective function in model 'univSat4' does not contain dimnames.
model <- mxModel("univSat4", mxMatrix(type = "Symm", nrow=1, ncol=1, free=T, values=1, name="expCov"), mxMatrix(type = "Full", nrow=1, ncol=1, free=T, values=0, name="expMean"), mxData(observed = testData, type="raw"), mxFIMLObjective(covariance = "expCov", means="expMean") # triggers error ) fit = mxRun(model)
Add dimnames to the covariance and means matrices:
mxMatrix("Symm", 1, 1, T, 1, name = "expCov", dimnames=list(selVars, selVars), mxMatrix("Full", 1,1, T, 0, name = "expMean", dimnames=list(selVars, selVars)),
mxFIMLObjective(covariance = "expCov", means="expMean", dimnames=selVars)
Suspect your start values, data, and mapping of model onto data. This model for instance will return an error with default start values
require(OpenMx) data(demoOneFactor) DV = "x4" threePred = c("x1", "x2", "x3") manifests = names(demoOneFactor) manifests = c(threePred, DV) dat = demoOneFactor[,manifests] fit1 = mxModel("base", type="RAM", latentVars = "G", manifestVars = manifests, mxPath(from = "G", arrows = 2, free = T), mxPath(from = manifests, arrows=2), # manifest variances mxPath(from = manifests, arrows=2, connect="unique.bivariate"), mxPath(from = manifests, to = "G"), mxData(cov(dat), type = "cov", numObs = 500) ) summary(mxRun(fit1, unsafe=T))
Starting the variances at their likely values will run.
fit1 = mxModel("base", type="RAM", latentVars = "G", manifestVars = manifests, mxPath(from = "G", arrows = 2, free = T), mxPath(from = manifests, arrows=2, values=diag(cov(dat))), # manifest variances mxPath(from = manifests, arrows=2, connect="unique.bivariate"), mxPath(from = manifests, to = "G"), mxData(cov(dat), type = "cov", numObs = 500) ) summary(mxRun(fit1, unsafe=T))