the continuous part of the model is not positive definite

require("OpenMx")
 
#start values
meanEMP1 <-mean(na.omit(D$empathy3Mom_1))
meanEMP2 <-mean(na.omit(D$empathy3Mom_2))
StartMeanEMP <-mean(c(meanEMP1,meanEMP2))
 
meanSOC1 <-mean(na.omit(D$sociableMom3_1))
meanSOC2 <-mean(na.omit(D$sociableMom3_2))
StartMeanSOC <-mean(c(meanSOC1,meanSOC2))
 
EMP <-c(D$empathy3Mom_1,D$empathy3Mom_2) 
StartVarEMP.ACE <-sqrt(var(na.omit(EMP))/3) 
 
SOC <-c(D$sociableMom3_1, D$sociableMom3_2) 
StartVarSOC.ACE <-sqrt(var(na.omit(SOC))/3)
 
# Select Variables for Analysis
selVars <- c('sociableMom3_1','sociableMom3_2','empathy3Mom_1','empathy3Mom_2')
aceVars <- c("A1_SOC","C1_SOC","E1_SOC","A2_SOC","C2_SOC","E2_SOC",
             "A1_EMP","C1_EMP","E1_EMP","A2_EMP","C2_EMP","E2_EMP")
 
# Select Data for Analysis
mz <- subset(D, zygosity==1,selVars)
dz <- subset(D, zygosity==2,selVars)
 
 
# Path objects for Multiple Groups
manifestVars=selVars
latentVars=aceVars
 
# specify paths for the variances and means of the latent variables
latVariances <- mxPath( from=aceVars, arrows=2,
                        free=FALSE, values=1 )
 
 
# means of latent variables
latMeans <- mxPath( from="one", to=aceVars, arrows=1,
                    free=FALSE, values=0 )
 
#specify paths for the means of the observed variables.
obsMeans <- mxPath( from="one", to=selVars, arrows=1,
                    free=c(T,T,T,T), 
                    values=c(StartMeanEMP, StartMeanEMP,StartMeanSOC, StartMeanSOC), 
                    labels=c("mean_EMP" ,"mean_EMP","mean_SOC","mean_SOC"
                    ) )
 
 
# path coefficients for twin 1. 
pathAceT1_SOC <- mxPath( from=c("A1_SOC","C1_SOC","E1_SOC"), 
                         to="sociableMom3_1", arrows=1, free=c(T,T,T), 
                         values=c(StartVarSOC.ACE,StartVarSOC.ACE,StartVarSOC.ACE),
                         label=c("aSOC","cSOC" ,"eSOC"))
 
 
 
pathAceT1_EMP <- mxPath( from=c("A1_SOC","C1_SOC","E1_SOC","A1_EMP","C1_EMP","E1_EMP"), 
                         to="empathy3Mom_1", arrows=1, free=c(T,T,T,T,T,T), 
                         values= c(StartVarSOC.ACE,StartVarSOC.ACE,StartVarSOC.ACE,
                                   StartVarEMP.ACE,StartVarEMP.ACE,StartVarEMP.ACE),
                         label=c("aSOC_EMP","cSOC_EMP" ,"eSOC_EMP","aEMP","cEMP" ,"eEMP") )
 
 
# path coefficients for twin 2
pathAceT2_SOC <- mxPath( from=c("A2_SOC","C2_SOC","E2_SOC"), 
                         to="sociableMom3_2", arrows=1, free=c(T,T,T), 
                         values=c(StartVarSOC.ACE,StartVarSOC.ACE,StartVarSOC.ACE),
                         label=c("aSOC","cSOC" ,"eSOC"))
 
 
 
pathAceT2_EMP <- mxPath( from=c("A2_SOC","C2_SOC","E2_SOC","A2_EMP","C2_EMP","E2_EMP"), 
                         to="empathy3Mom_1", arrows=1, free=c(T,T,T,T,T,T), 
                         values= c(StartVarSOC.ACE,StartVarSOC.ACE,StartVarSOC.ACE,StartVarEMP.ACE,StartVarEMP.ACE,StartVarEMP.ACE),
                         label=c("aSOC_EMP","cSOC_EMP" ,"eSOC_EMP","aEMP","cEMP" ,"eEMP"))
 
 
# covariance between C1 & C2
covC1C2_SOC <- mxPath( from="C1_SOC", to="C2_SOC", arrows=2,free=FALSE, values=1 )
covC1C2_EMP <- mxPath( from="C1_EMP", to="C2_EMP", arrows=2,free=FALSE, values=1 )
 
 
# covariance between A1 & A2 in MZ's
covA1A2_MZ_SOC <-mxPath( from="A1_SOC", to="A2_SOC", arrows=2, free=FALSE,values=1 )
covA1A2_MZ_EMP <-mxPath( from="A1_EMP", to="A2_EMP", arrows=2, free=FALSE,values=1 )
# covariance between A1 & A2 in DZ's
covA1A2_DZ_SOC <-mxPath( from="A1_SOC", to="A2_SOC",arrows=2, free=FALSE,values=.5 )
covA1A2_DZ_EMP <-mxPath( from="A1_EMP", to="A2_EMP",arrows=2, free=FALSE,values=.5 )
 
#call the data.frame with the MZ raw data, mzData, and the DZ raw data
dataMZ <- mxData( observed=mz, type="raw" )
dataDZ <- mxData( observed=dz, type="raw" )
 
# Combine Groups
#arange together all the paths that are the same for MZ and DZ
paths <- list(latVariances, latMeans, obsMeans,pathAceT1_SOC,
              pathAceT2_SOC,pathAceT1_EMP,pathAceT2_EMP,covC1C2_SOC,covC1C2_EMP)
 
 
#Build a model seperately for MZ and DZ and for each sex
modelMZ <- mxModel(model="MZ", type="RAM", manifestVars=selVars,
                   latentVars=aceVars, paths, covA1A2_MZ_SOC
                   ,covA1A2_MZ_EMP, dataMZ)
modelDZ <- mxModel(model="DZ", type="RAM", manifestVars=selVars,
                   latentVars=aceVars, paths,covA1A2_DZ_SOC
                   ,covA1A2_DZ_EMP, dataDZ )
 
 
#Ask for confidence intervals
CI <-mxCI(reference = c('aSOC','cSOC','eSOC','aSOC_EMP','cSOC_EMP','eSOC_EMP','aEMP','cEMP','eEMP'), 
          interval = 0.95, type=c("both", "lower", "upper"))
 
obj <- mxFitFunctionMultigroup(c("MZ","DZ"))
modelACE.Cholesky <- mxModel(model="ACE_Cholesky", modelMZ, modelDZ, obj, CI )
 
#mxOption(NULL, "Default optimizer", "NPSOL")
 
fitACE.Cholesky <- mxRun(modelACE.Cholesky, intervals=TRUE)