BootstrapParallel error

I took the nuke option wrt OpenMx:

mv OpenMx OpenMx.old
svn checkout --username me http://openmx.psyc.virginia.edu/svn OpenMx
cd OpenMx/trunk
sudo make install


run again

same errors (maybe warnings only?) - though it does seem to run ok:

Error in checkForRemoteErrors(val) :
1000 nodes produced errors; first error: unused argument(s) (useOptimizer)
>
> results <- t(omxSapply(modelResults@submodels, getStats))
>
> sfStop()

Stopping cluster

>
> # get rid of bad covergence results
> results2 <- data.frame(results[which(results[,1] <= 1),])
>
> # summarize the results
> means <- mean(results2)
> stdevs <- sd(results2)
> sumResults <- data.frame(matrix(dnr[pStrt:pEnd], 2*nVar, 1,
+ dimnames=list(NULL, "Parameter")))
> sumResults$mean <- means[pStrt:pEnd]
> sumResults$obsStDev <- stdevs[pStrt:pEnd]
> sumResults$meanHessEst <- means[hStrt:hEnd]
> sumResults$sqrt2meanHessEst <- sqrt(2) * sumResults$meanHessEst
>
> omxCheckCloseEnough(sumResults[1,'mean'], 0.8, 0.1)
sumResults[1, "mean"] and 0.8 are equal to within 0.1.
> omxCheckCloseEnough(sumResults[2,'mean'], 0.5, 0.1)
sumResults[2, "mean"] and 0.5 are equal to within 0.1.
> omxCheckCloseEnough(sumResults[3,'mean'], 0.7, 0.1)
sumResults[3, "mean"] and 0.7 are equal to within 0.1.
> omxCheckCloseEnough(sumResults[4,'mean'], 0.0, 0.1)
sumResults[4, "mean"] and 0 are equal to within 0.1.
> omxCheckCloseEnough(sumResults[5,'mean'], 1.00, 0.1)
sumResults[5, "mean"] and 1 are equal to within 0.1.
> omxCheckCloseEnough(sumResults[6,'mean'], 1.00, 0.1)
sumResults[6, "mean"] and 1 are equal to within 0.1.
> omxCheckCloseEnough(sumResults[7,'mean'], 1.00, 0.1)
sumResults[7, "mean"] and 1 are equal to within 0.1.
> omxCheckCloseEnough(sumResults[8,'mean'], 1.00, 0.1)
sumResults[8, "mean"] and 1 are equal to within 0.1.
>
> detach("package:snowfall")
> detach("package:snow")
> traceback()
8: stop(count, " nodes produced errors; first error: ", firstmsg)
7: checkForRemoteErrors(val)
6: dynamicClusterApply(cl, fun, length(x), argfun)
5: clusterApplyLB(sfGetCluster(), x, fun, ...)
4: sfClusterApplyLB(x, fun, ...)
3: omxLapply(independents, mxRun, intervals = intervals, silent = silent,
suppressWarnings = suppressWarnings, unsafe = unsafe, checkpoint = checkpoint,
useSocket = useSocket, onlyFrontend = onlyFrontend, useOptimizer = useOptimizer)
2: runHelper(model, frontendStart, intervals, silent, suppressWarnings,
unsafe, checkpoint, useSocket, onlyFrontend, useOptimizer)
1: mxRun(topModel, silent = TRUE, suppressWarnings = TRUE)

One thing that's a bit odd though. When it is running mxRun step, I only seem to see one R process. When it summarizes results it seems to use 4 (as desired on new Macbook Pro). I had to increase the nReps to some 100,000 to see this behavior. So it looks like

modelResults <- mxRun(topModel, silent=TRUE, suppressWarnings=TRUE)

Uses 1 processor, whereas

results <- t(omxSapply(modelResults@submodels, getStats))

uses 4.