Decomposed ACE variances...now trying to run regressions

# Select Variables for Analysis
selVars   <- c('cohenpss_score_A','cohenpss_score_B') #Selecting these columns for analysis
aceVars   <- c("A1","C1","E1","A2","C2","E2") #creating latent variables
 
# Select Data for Analysis: Creating two subsets for MZ and DZ to generate descriptive stats for each group type
mzData    <- subset(df, zyg_A==1, selVars)
dzData    <- subset(df, zyg_A==0, selVars)
 
# Generate Descriptive Statistics
colMeans(mzData,na.rm=TRUE) #mean ~11
colMeans(dzData,na.rm=TRUE)
cov(mzData,use="complete") #cov ~45
cov(dzData,use="complete")
 
# Path objects for Multiple Groups
manifestVars=selVars #Manifest = the Stress phenotype
latentVars=aceVars 
 
# variances of latent variables
latVariances <- mxPath( from=aceVars, arrows=2,
                        free=FALSE, values=1 )
 
# means of latent variables... AND SPECIFYING SINGLE=HEADED ARROWS FROM TRIANGLE (WHICH IS FIXED AT 1) TO EACH OF THE LATENT VARIABLES, FIXED AT 0
 
latMeans     <- mxPath( from="one", to=aceVars, arrows=1,
                        free=FALSE, values=0 )
 
# means of observed variables 
 
obsMeans     <- mxPath( from="one", to=selVars, arrows=1,
                        free=TRUE, values=10, labels="mean" ) #The mean cohen_pss_score is around 11 
 
# path coefficients for twin 1
pathAceT1    <- mxPath( from=c("A1","C1","E1"), to="cohenpss_score_A", arrows=1,
                        free=TRUE, values=.5,  label=c("a","c","e") )
# path coefficients for twin 2
pathAceT2    <- mxPath( from=c("A2","C2","E2"), to="cohenpss_score_B", arrows=1,
                        free=TRUE, values=.5,  label=c("a","c","e") )
 
#The common environmental factors are the same for both twins (by definition), so setting the fixed correlation of 1 for C1 and C2
 
# covariance between C1 & C2
covC1C2      <- mxPath( from="C1", to="C2", arrows=2,
                        free=FALSE, values=1 )
 
 
# covariance between A1 & A2 in MZ twins
covA1A2_MZ   <- mxPath( from="A1", to="A2", arrows=2,
                        free=FALSE, values=1 )
# covariance between A1 & A2 in DZ twins
covA1A2_DZ   <- mxPath( from="A1", to="A2", arrows=2,
                        free=FALSE, values=.5 )
 
 
# Data objects for Multiple Groups
dataMZ       <- mxData( observed=mzData, type="raw" )
dataDZ       <- mxData( observed=dzData, type="raw" )
 
# Combine Groups
paths        <- list( latVariances, latMeans, obsMeans,
                      pathAceT1, pathAceT2, covC1C2 )
modelMZ      <- mxModel(model="MZ", type="RAM", manifestVars=selVars,
                        latentVars=aceVars, paths, covA1A2_MZ, dataMZ )
modelDZ      <- mxModel(model="DZ", type="RAM", manifestVars=selVars,
                        latentVars=aceVars, paths, covA1A2_DZ, dataDZ )
 
obj          <- mxFitFunctionMultigroup(c("MZ", "DZ"))
modelACE     <- mxModel(model="ACE", modelMZ, modelDZ, obj )
 
# Run Model
fitACE       <- mxRun(modelACE)
sumACE       <- summary(fitACE)
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