Question about Confidence Interval for Standardized path coefficients (multivariate model)

Hi everyone, I am new here and learning OpenMX. I have a problem of getting confidence interval for standardized path coefficients. Could anyone please provide me the OpenMx language about this? I also provide my scripts below and hope someone can let me know what the problem is (the script about 'ciACE <- mxCI("VC[1:5, 16:30]")' ) or how to read the output about standardized CI.

# Load Data
nl <- read.csv(file="C:/R/TwinPairACE.csv", header=TRUE, sep=",")

# Recode Data for Analysis - Rescale variables to have variances around 1.0
nl$PC1 <- nl$PAIRC1/9.6
nl$PC2 <- nl$PAIRC2/9.6
nl$BDS1 <- nl$BDS1/2.4
nl$BDS2 <- nl$BDS2/2.4
nl$CHEXI_RE1 <- nl$CHEXI_RE1/18.5
nl$CHEXI_RE2 <- nl$CHEXI_RE2/18.5
nl$CVK1 <- nl$CVK1/9.7
nl$CVK2 <- nl$CVK2/9.7
nl$EVK1 <- nl$EVK1/12
nl$EVK2 <- nl$EVK2/12

# Select Variables for Analysis
vars <- c('PC','BDS','CHEXI_RE','CVK','EVK')
nv <- 5 # number of variables
ntv <- nv*2 # number of total variables
selVars <- paste(vars,c(rep(1,nv),rep(2,nv)),sep="")

# Select Covariates for Analysis
nl[,'Ageyr_Ind'] <- nl[,'Ageyr_Ind']/100
# nl <- nl[-which(is.na(nl$Ageyr_Ind)),] # missing value
covVars <- 'Ageyr_Ind'
nc <- nv # number of covariates

# Select Data for Analysis
mzData <- subset(nl, ZygTwin==1, c(selVars, covVars))
dzData <- subset(nl, ZygTwin==2, c(selVars, covVars))

# Set Starting Values
svMe <- c(7,5,5,1,1) # start value for means
svPa <- .4 # start value for path coefficient
svPaD <- vech(diag(svPa,nv,nv)) # start values for diagonal of covariance matrix
svPe <- .8 # start value for path coefficient for e
svPeD <- vech(diag(svPe,nv,nv)) # start values for diagonal of covariance matrix
lbPa <- .0001 # start value for lower bounds
lbPaD <- diag(lbPa,nv,nv) # lower bounds for diagonal of covariance matrix
lbPaD[lower.tri(lbPaD)] <- -10 # lower bounds for below diagonal elements
lbPaD[upper.tri(lbPaD)] <- NA # lower bounds for above diagonal elements

# Create Labels
labMe <- paste("mean",vars,sep="_")
labBe <- labFull("beta",nc,1)

# ------------------------------------------------------------------------------
# PREPARE MODEL

# ACE Model
# Create Matrices for Covariates and linear Regression Coefficients
defAge <- mxMatrix( type="Full", nrow=1, ncol=1, free=FALSE, labels=c("data.Ageyr_Ind"), name="Age" )
pathB <- mxMatrix( type="Full", nrow=nc, ncol=1, free=TRUE, values=.01, label=labBe, name="b" )

# Create Algebra for expected Mean Matrices
meanG <- mxMatrix( type="Full", nrow=1, ncol=ntv, free=TRUE, values=svMe, labels=labMe, name="meanG" )
expMean <- mxAlgebra( expression= meanG + cbind(t(b%*%Age),t(b%*%Age)), name="expMean" )

# Create Matrices for Path Coefficients
pathA <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPaD, label=labLower("a",nv), lbound=lbPaD, name="a" )
pathC <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPaD, label=labLower("c",nv), lbound=lbPaD, name="c" )
pathE <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPeD, label=labLower("e",nv), lbound=lbPaD, name="e" )

# Create Algebra for 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" )

# Create Algebra for expected Variance/Covariance Matrices in MZ & DZ twins
covP <- mxAlgebra( expression= A+C+E, name="V" )
covMZ <- mxAlgebra( expression= A+C, name="cMZ" )
covDZ <- mxAlgebra( expression= 0.5%x%A+ C, name="cDZ" )
expCovMZ <- mxAlgebra( expression= rbind( cbind(V, cMZ), cbind(t(cMZ), V)), name="expCovMZ" )
expCovDZ <- mxAlgebra( expression= rbind( cbind(V, cDZ), cbind(t(cDZ), V)), name="expCovDZ" )

# Create Algebra for Standardization
matI <- mxMatrix( type="Iden", nrow=nv, ncol=nv, name="I")
invSD <- mxAlgebra( expression=solve(sqrt(I*V)), name="iSD")

# Calculate genetic and environmental correlations
corA <- mxAlgebra( expression=solve(sqrt(I*A))%&%A, name ="rA" )
corC <- mxAlgebra( expression=solve(sqrt(I*C))%&%C, name ="rC" )
corE <- mxAlgebra( expression=solve(sqrt(I*E))%&%E, name ="rE" )

# Create Data Objects for Multiple Groups
dataMZ <- mxData( observed=mzData, type="raw" )
dataDZ <- mxData( observed=dzData, type="raw" )

# Create Expectation Objects for Multiple Groups
expMZ <- mxExpectationNormal( covariance="expCovMZ", means="expMean", dimnames=selVars )
expDZ <- mxExpectationNormal( covariance="expCovDZ", means="expMean", dimnames=selVars )
funML <- mxFitFunctionML()

# Create Model Objects for Multiple Groups
pars <- list(pathB, meanG, matI, invSD,
pathA, pathC, pathE, covA, covC, covE, covP, corA, corC, corE)
defs <- list(defAge)
modelMZ <- mxModel( name="MZ", pars, defs, expMean, covMZ, expCovMZ, dataMZ, expMZ, funML )
modelDZ <- mxModel( name="DZ", pars, defs, expMean, covDZ, expCovDZ, dataDZ, expDZ, funML )
multi <- mxFitFunctionMultigroup( c("MZ","DZ") )

# Create Algebra for Variance Components
rowVC <- rep('VC',nv)
colVC <- rep(c('A','C','E','SA','SC','SE'),each=nv)
estVC <- mxAlgebra( expression=cbind(A,C,E,A/V,C/V,E/V), name="VC", dimnames=list(rowVC,colVC))

# Create Confidence Interval Objects
ciACE <- mxCI( "VC[1:5,16:30]" )

# Build Model with Confidence Intervals
modelACE <- mxModel( "mulACEEF", pars, modelMZ, modelDZ, multi, estVC, ciACE )

# Run ACE Model
fitACE <- mxRun( modelACE, intervals=T )
summary( fitACE, verbose=T )

# ACE Estimated Path Coefficients
matACEepaths <- c("a","c","e","iSD")
labACEepaths <- c("PathA","PathC","PathE","iSD")
formatOutputMatrices(fitACE, matACEepaths, labACEepaths, vars,4)

# ACE Standardized Path Coefficients (pre-multiplied by inverse of standard deviations)
matACEpaths <- c("iSD %*% a","iSD %*% c","iSD %*% e")
labACEpaths <- c("stPathA","stPathC","stPathE")
formatOutputMatrices(fitACE, matACEpaths, labACEpaths, vars,4)

Many thanks!