Can't derive CIs in multiple groups twin model. Too nested!

> super_o <- mxRun(super, intervals = T)
trace: mxRun(super, intervals = T)
Error: evaluation nested too deeply: infinite recursion / options(expressions=)?

require(OpenMx)
require(dplyr)
 
# Load Data
data(twinData)
 
 
# Select Variables for Analysis
Vars      <- 'bmi'
nv        <- 1       # number of variables
ntv       <- nv*2    # number of total variables
selVars   <- paste(Vars,c(rep(1,nv),rep(2,nv)),sep="")   #c('bmi1','bmi2')
 
# Select Data for Analysis
 
mzData_m <- twinData |> filter(zygosity == "MZMM") |> select(bmi1, bmi2)
mzData_f <- twinData |> filter(zygosity == "MZFF") |> select(bmi1, bmi2)
 
dzData_m <- twinData |> filter(zygosity == "DZMM") |> select(bmi1, bmi2)
dzData_f <- twinData |> filter(zygosity == "DZFF") |> select(bmi1, bmi2)
 
# Prepare Data
# -----------------------------------------------------------------------------
 
 
# Set Starting Values
svMe      <- 20      # start value for means
svPa      <- .5      # start value for path coefficients (sqrt(variance/#ofpaths))
 
# ACE Model
# Matrices declared to store a, d, and e Path Coefficients
pathA     <- mxMatrix( type="Full", nrow=nv, ncol=nv, 
                       free=TRUE, values=svPa, label="a11", name="m_a" ) 
pathC     <- mxMatrix( type="Full", nrow=nv, ncol=nv, 
                       free=TRUE, values=svPa, label="c11", name="m_c" )
pathE     <- mxMatrix( type="Full", nrow=nv, ncol=nv, 
                       free=TRUE, values=svPa, label="e11", name="m_e" )
 
# Matrices generated to hold A, C, and E computed Variance Components
covA      <- mxAlgebra( expression=m_a %*% t(m_a), name="A" )
covC      <- mxAlgebra( expression=m_c %*% t(m_c), name="C" ) 
covE      <- mxAlgebra( expression=m_e %*% t(m_e), name="E" )
 
# Algebra to compute total variances
covP      <- mxAlgebra( expression=A+C+E, name="V" )
 
# Algebra for expected Mean and Variance/Covariance Matrices in MZ & DZ twins
meanG     <- mxMatrix( type="Full", nrow=1, ncol=ntv, 
                       free=TRUE, values=svMe, label="mean", 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" )
 
# Data objects for Multiple Groups
dataMZ    <- mxData( observed=mzData_m, type="raw" )
dataDZ    <- mxData( observed=dzData_f, type="raw" )
 
# Objective objects for Multiple Groups
expMZ     <- mxExpectationNormal( covariance="expCovMZ", means="expMean", 
                                  dimnames=selVars )
expDZ     <- mxExpectationNormal( covariance="expCovDZ", means="expMean", 
                                  dimnames=selVars )
funML     <- mxFitFunctionML()
 
# Combine Groups
pars      <- list( pathA, pathC, pathE, covA, covC, covE, covP )
modelMZ   <- mxModel( pars, meanG, covMZ, dataMZ, expMZ, funML, name="MZ" )
modelDZ   <- mxModel( pars, meanG, covDZ, dataDZ, expDZ, funML, name="DZ" )
fitML     <- mxFitFunctionMultigroup(c("MZ", "DZ") )
ci <- mxCI("m_a")
twinACEModel  <- mxModel( "ACE_m", pars, modelMZ, modelDZ, fitML ,ci)
 
# Run Model
twinACEFit   <- mxRun(twinACEModel, intervals=T)
summary(twinACEFit)
 
######  AGAIN FOR FEMALES
 
# ACE Model
# Matrices declared to store a, d, and e Path Coefficients
pathA     <- mxMatrix( type="Full", nrow=nv, ncol=nv, 
                       free=TRUE, values=svPa, label="a11", name="f_a" ) 
pathC     <- mxMatrix( type="Full", nrow=nv, ncol=nv, 
                       free=TRUE, values=svPa, label="c11", name="f_c" )
pathE     <- mxMatrix( type="Full", nrow=nv, ncol=nv, 
                       free=TRUE, values=svPa, label="e11", name="f_e" )
 
# Matrices generated to hold A, C, and E computed Variance Components
covA      <- mxAlgebra( expression=f_a %*% t(f_a), name="A" )
covC      <- mxAlgebra( expression=f_c %*% t(f_c), name="C" ) 
covE      <- mxAlgebra( expression=f_e %*% t(f_e), name="E" )
 
# Algebra to compute total variances
covP      <- mxAlgebra( expression=A+C+E, name="V" )
 
# Algebra for expected Mean and Variance/Covariance Matrices in MZ & DZ twins
meanG     <- mxMatrix( type="Full", nrow=1, ncol=ntv, 
                       free=TRUE, values=svMe, label="mean", 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" )
 
# Data objects for Multiple Groups
dataMZ    <- mxData( observed=mzData_m, type="raw" )
dataDZ    <- mxData( observed=dzData_f, type="raw" )
 
# Objective objects for Multiple Groups
expMZ     <- mxExpectationNormal( covariance="expCovMZ", means="expMean", 
                                  dimnames=selVars )
expDZ     <- mxExpectationNormal( covariance="expCovDZ", means="expMean", 
                                  dimnames=selVars )
funML     <- mxFitFunctionML()
 
# Combine Groups
pars      <- list( pathA, pathC, pathE, covA, covC, covE, covP )
modelMZ   <- mxModel( pars, meanG, covMZ, dataMZ, expMZ, funML, name="MZ_f" )
modelDZ   <- mxModel( pars, meanG, covDZ, dataDZ, expDZ, funML, name="DZ_f" )
fitML     <- mxFitFunctionMultigroup(c("MZ_f", "DZ_f") )
ci <- mxCI("f_a")
twinACEModel_f  <- mxModel( "ACE_f", pars, modelMZ, modelDZ, fitML ,ci)
 
# Run Model
twinACEFit_f   <- mxRun(twinACEModel_f, intervals=T)
summary(twinACEFit_f)
 
 
### Supermodel
 
super <- mxModel("super", twinACEModel, twinACEModel_f, 
    mxFitFunctionMultigroup(groups = c("ACE_m", "ACE_f")))
 
super_o <- mxRun(super, intervals = T)
summary(super_o)