Error in if (class(fit) == "try-error" || is.na(fit$output$minimum) || : missing value where TRUE/FALSE needed

I'm running a measurement model (two time point common factor model) and I am getting an error message I've never seen before when I drop a parameter. I talked to AdminRobK about this, and he suggested I post it to the forums. The error reads:

Error in if (class(fit) == "try-error" || is.na(fit$output$minimum) || : missing value where TRUE/FALSE needed
In addition: Warning message:
In is.na(fit$output$minimum) : is.na() applied to non-(list or vector) of type 'NULL'

This only happens when I include the 'intervals=T' option. If this option is removed, it runs fine. Script is below (and attached).

My system information:
OpenMx version: 2.3.1 [GIT v2.3.1]
R version: R version 3.2.3 (2015-12-10)
Platform: x86_64-apple-darwin13.4.0
MacOS: 10.11.2
Default optimiser: NPSOL

require(OpenMx)

mzData <- cbind(1+rnorm(100, mean=.5, sd=.5),1+rnorm(100, mean=.5, sd=.5),1+rnorm(100, mean=.5, sd=.5),1+rnorm(100, mean=.5, sd=.5))
colnames(mzData) <- c("t1_v1", "t1_v2", "t2_v1", "t2_v2")

dzData <- cbind(1+rnorm(100, mean=.5, sd=.5),1+rnorm(100, mean=.5, sd=.5),1+rnorm(100, mean=.5, sd=.5),1+rnorm(100, mean=.5, sd=.5))
colnames(dzData) <- c("t1_v1", "t1_v2", "t2_v1", "t2_v2")

nv <- 2 # number of variables
ntv <- nv*2 # number of total variables
selVars <- c("t1_v1", "t1_v2", "t2_v1", "t2_v2")

nf <- 1

laAs <- paste("as",1:nv,1:nv,sep="")
laCs <- paste("cs",1:nv,1:nv,sep="")
laEs <- c("es_1_1", "es_2_2")
laFl <- "load"

svMe <- c(.5,.5,.5,.5)
laMe <- paste(selVars,"Mean",sep="")
laMeMZ <- paste(selVars,"MeanMZ",sep="")
laMeDZ <- paste(selVars,"MeanDZ",sep="_")

pathAl <- mxMatrix( type="Lower", nrow=nf, ncol=nf, free=TRUE, values=.5, labels="al_1_1", lbound=.00001, name="al" )
pathCl <- mxMatrix( type="Lower", nrow=nf, ncol=nf, free=TRUE, values=.5, labels="cl_1_1", lbound=.00001, name="cl" )
pathEl <- mxMatrix( type="Lower", nrow=nf, ncol=nf, free=TRUE, values=.5, labels="el_1_1", lbound=.00001, name="el" )

covarLP <- mxAlgebra( expression= al %% t(al) + cl %% t(cl) + el %*% t(el), name="CovarLP" )
varLP <- mxAlgebra( expression= diag2vec(CovarLP), name="VarLP" )
unit <- mxMatrix( type="Unit", nrow=nf, ncol=1, name="Unit")
varLP1 <- mxConstraint( expression=VarLP == Unit, name="varLP1")

pathF <- mxMatrix( type="Full", nrow=nv, ncol=nf, free=TRUE, values=.5, labels=laFl, name="fl", lbound=.00001 )

pathEs <- mxMatrix( type="Diag", nrow=nv, ncol=nv, free=TRUE, values=.5, labels=laEs, lbound=.00001, name="es" )

covA <- mxAlgebra( expression=fl %&% (al %% t(al)), name="A" )
covC <- mxAlgebra( expression=fl %&% (cl %% t(cl)), name="C" )
covE <- mxAlgebra( expression=fl %&% (el %% t(el)) + es %% t(es), name="E" )

covP <- mxAlgebra( expression=A+C+E, name="V" )
matI <- mxMatrix( type="Iden", nrow=nv, ncol=nv, name="I")
invSD <- mxAlgebra( expression=solve(sqrt(I*V)), name="iSD")

meanG <- mxMatrix( type="Full", nrow=1, ncol=ntv, free=TRUE, values=svMe, label=c("meanv1", "meanv2", "meanv1", "meanv2"), name="expMean" )
covMZ <- mxAlgebra( expression= rbind( cbind(V , A+C), cbind(A+C , V)), name="expCovMZ" )
covDZ <- mxAlgebra( expression= rbind( cbind(V, 0.5%x%A+C), cbind(0.5%x%A+C ,V)), name="expCovDZ" )

dataMZ <- mxData( observed=mzData, type="raw" )
dataDZ <- mxData( observed=dzData, type="raw" )

expMZ <- mxExpectationNormal( covariance="expCovMZ", means="expMean", dimnames=selVars )
expDZ <- mxExpectationNormal( covariance="expCovDZ", means="expMean", dimnames=selVars )
funML <- mxFitFunctionML()

pars <- list( pathAl, pathCl, pathEl, covarLP, varLP, unit, pathF, pathEs, covA, covC, covE, covP, matI, invSD)
modelMZ <- mxModel( "MZ", pars, covMZ, meanG, dataMZ, expMZ, funML )
modelDZ <- mxModel( "DZ", pars, covDZ, meanG, dataDZ, expDZ, funML )
minus2ll <- mxAlgebra( MZ.objective+ DZ.objective, name="minus2sumloglikelihood" )
obj <- mxFitFunctionAlgebra( "minus2sumloglikelihood" )
ComAceModel <- mxModel( "ComACE", pars, varLP1, modelMZ, modelDZ, minus2ll, obj, mxCI(c('al', 'cl', 'el', 'fl', 'es')))

ComAceFit <- mxRun(ComAceModel, intervals=T)
ComAceFit <- mxTryHard(ComAceModel, intervals=T)
ComAceSum <- summary(ComAceFit)
ComAceSum

ComAeFit <- mxModel(ComAceFit, name="ComAeFit" )
ComAeFit <- omxSetParameters(ComAeFit, labels="cl_1_1", values=0, free=F)
ComAeFit <- mxRun(ComAeFit, intervals=T)
ComAeFit <- mxTryHard(ComAeFit, intervals=T)
ComAeSum <- summary(ComAeFit)
ComAeSum