Multivariate Covariance Estimate CI's

I'm currently runnning a three variable Cholesky ACE model and am able to grab CI's for my variance estimates and correlations but my model crashes and gives me an NLOPT fatal error -1 when I attempt to retrieve confidence intervals for covariance estimates. I'm wondering if anyone can tell me why and how to retrieve the problematic CI's. Here's my script:

# -----------------------------------------------------------------------------------------------------------
# Script: 2ACE_Chol.R

# Fits a MV Cholesky ACE Decomposition
# Data File: Covariate Regressed Cross-trait Data.csv
# Phenotypes: Facial masculinity preference, rated facial masculinity, morphometric facial masculinity.
# Type of data: Raw continuous data
# ZYG: 1=MZF, 2= MZM, 3 = DZF, 4 = DZM
# Assumptions: means and variances can be equated across birth order & zygosity groups
# ------------------------------------------------------------------------------------------------------------

#directory
setwd("~/Desktop/PhD/Twin Modelling/OpenMX/Cross-twin Masc Pref")

# Load Libraries
require(OpenMx)
require(psych)
source("~/Desktop/PhD/Twin Modelling/OpenMx/GenEpiHelperFunctions.R")

#data <- read.csv('Covariate Regressed Cross-trait Data.csv')
data <- read.csv('Covariate Regressed Non-Standardized Cross-trait Data.csv')

data$FaceP1 <- data$FacePref1
data$RMasc1 <- data$Ratedmasc1
data$MMasc1 <- data$Morphmasc1
data$FaceP2 <- data$FacePref2
data$RMasc2 <- data$Ratedmasc2
data$MMasc2 <- data$Morphmasc2

nv <- 3 # number of varPBbles
nt <- 2 # number twins/siblings
ntv <- nv*nt # number of total varPBbles #12

varnames <- c('FaceP','RMasc','MMasc')
selVars <- c('FaceP1', 'RMasc1','MMasc1',
'FaceP2', 'RMasc2','MMasc2')

#FEMALES
mzdata <- subset(data, Zygosity==1, selVars)
dzdata <- subset(data, Zygosity==3, selVars)
describe(mzdata)
describe(dzdata)

# Create start values
Stmean <-colMeans(mzdata[,1:3],na.rm=TRUE)

# Create Labels for Lower Triangular Matrices
aLabs <- paste("a", do.call(c, sapply(seq(1, nv), function(x){ paste(x:nv, x,sep="") })), sep="")
cLabs <- paste("c", do.call(c, sapply(seq(1, nv), function(x){ paste(x:nv, x,sep="") })), sep="")
eLabs <- paste("e", do.call(c, sapply(seq(1, nv), function(x){ paste(x:nv, x,sep="") })), sep="")

# Create Labels for Column and Diagonal Matrices
mLabs <- paste("m",1:nv,sep="")

# Specify Cholesky ACE Decomposition
# -------------------------------------------------------------------------------------------------
# Matrices declared to store a, c, and e Path Coefficients
pathA <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=.4, labels=aLabs, name="a" )
pathC <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=.1, labels=cLabs, name="c" )
pathE <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=.2, labels=eLabs, name="e" )

# Matrices generated to hold A, C, and E computed Variance Components
covA <- mxAlgebra( expression=a %*% t(a), name="A" )
covC <- mxAlgebra( expression=c %*% t(c), name="C" )
covE <- mxAlgebra( expression=e %*% t(e), name="E" )

# Algebra to compute total variances and standard deviations (diagonal only)
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")

# Algebra to compute Correlations
invSDa<- mxAlgebra( expression=solve(sqrt(I*A)), name="iSDa")
invSDc<- mxAlgebra( expression=solve(sqrt(I*C)), name="iSDc")
invSDe<- mxAlgebra( expression=solve(sqrt(I*E)), name="iSDe")

Rph <- mxAlgebra( expression=iSD %&% V , name="Phcor")
Rg <- mxAlgebra( expression=iSDa %&% A , name="Acor")
Rc <- mxAlgebra( expression=iSDc %&% C , name="Ccor")
Re <- mxAlgebra( expression=iSDe %&% E , name="Ecor")

# Algebras to compute Standardized Variance Components
rowVars <- rep('v',nv)
colVars <- rep(c('A','C','E','h2','c2','e2'),each=nv)
estVars <- mxAlgebra( expression=cbind(A,C,E,A/V,C/V,E/V), name="est", dimnames=list(rowVars,colVars))

# Algebra for expected Mean and Variance/Covariance Matrices in MZ & DZ twins

MeanG <- mxMatrix( type="Full", nrow=1, ncol=ntv, free=TRUE, values=Stmean, labels=c(mLabs,mLabs), name="expMean" )

covMZ <- mxAlgebra( expression= rbind( cbind(A+C+E , A+C),
cbind(A+C , A+C+E)), name="expCovMZ" )
covDZ <- mxAlgebra( expression= rbind( cbind(A+C+E , 0.5%x%A+C),
cbind(0.5%x%A+C , A+C+E)), name="expCovDZ" )

dataMZ <- mxData( observed=mzdata, type="raw" )
dataDZ <- mxData( observed=dzdata, type="raw" )

expfunctionMZ <- mxExpectationNormal("expCovMZ", "expMean", dimnames = selVars)
objMZ <- mxFitFunctionML()
expfunctionDZ <- mxExpectationNormal("expCovDZ", "expMean", dimnames = selVars)
objDZ <- mxFitFunctionML()

# Combine Groups
pars <- list( pathA, pathC, pathE, covA, covC, covE, covP, matI, invSD, invSDa, invSDc, invSDe, Rph, Rg, Rc, Re, estVars )
modelMZ <- mxModel( pars, covMZ, MeanG, dataMZ, expfunctionMZ, objMZ, name="MZ" )
modelDZ <- mxModel( pars, covDZ, MeanG, dataDZ, expfunctionDZ, objDZ, name="DZ" )
minus2ll <- mxAlgebra( MZ.objective + DZ.objective, name="minus2sumloglikelihood" )
obj <- mxFitFunctionAlgebra("minus2sumloglikelihood")
h2 <- mxCI (c ('MZ.est[1,10]', 'MZ.est[2,11]', 'MZ.est[3,12]') )
h2cov <- mxCI (c ('MZ.est[1,11]', 'MZ.est[1,12]', 'MZ.est[2,12]') ) #h2cov
c2 <- mxCI (c ('MZ.est[1,13]', 'MZ.est[2,14]', 'MZ.est[3,15]') ) #c2
c2cov <- mxCI (c ('MZ.est[1,14]', 'MZ.est[1,15]', 'MZ.est[2,15]') ) #c2cov
e2 <- mxCI (c ('MZ.est[1,16]', 'MZ.est[2,17]', 'MZ.est[3,18]') ) #e2
e2cov <- mxCI (c ('MZ.est[1,19]', 'MZ.est[1,20]', 'MZ.est[2,20]') ) #e2cov
pheno <- mxCI (c ('MZ.Phcor[1,2]','MZ.Phcor[1,3]', 'MZ.Phcor[2,3]') ) #rph
rg <- mxCI (c ('MZ.Acor[2,1]', 'MZ.Acor[3,1]', 'MZ.Acor[3,2]') ) # Rg
rc <- mxCI (c ('MZ.Ccor[2,1]', 'MZ.Ccor[3,1]', 'MZ.Ccor[3,2]') ) # Rc
re <- mxCI (c ('MZ.Ecor[2,1]', 'MZ.Ecor[3,1]', 'MZ.Ecor[3,2]') ) # Re
CIs <- list( h2, h2cov, c2, c2cov, e2, e2cov, pheno, rg, rc, re )
CholAceModel<- mxModel( "CholACE", pars, modelMZ, modelDZ, minus2ll, obj, CIs)
CholAceFit <- mxRun(CholAceModel, intervals = TRUE)