Hi I am doing some univariate analyses but I have one doubt when about how getting the intraclass correlations for MZ and DZ. I have tried with cov2cor but I can not make that work. Here is the script:
# Select Variables for Analysis vars <- "AG_Ln" # list of variables names nv <- 1 # 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 #twinData[,'age'] <- twinData[,'age']/100 #twinData <- twinData[-which(is.na(twinData$age)),] covVars <- c("age","Sex1","Sex2") # Select Data for Analysis mzData <- subset(twinData, Zyg==1, c(selVars, covVars)) dzData <- subset(twinData, Zyg==2, c(selVars, covVars)) # Set Starting Values svMe <- 4 # start value for means svPa <- .4 # start value for path coefficient svPe <- .8 # start value for path coefficient for e lbPa <- .01 # start value for lower bounds # ------------------------------------------------------------------------------ # 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.age"), name="Age" ) pathB1 <- mxMatrix( type="Full", nrow=1, ncol=1, free=TRUE, values=.01, label="b11", name="b1" ) defSex <- mxMatrix( type="Full", nrow=1, ncol=2, free=FALSE, labels=c("data.Sex1", "data.Sex2"), name="Sex" ) pathB2 <- mxMatrix( type="Full", nrow=1, ncol=1, free=TRUE, values=.01, label="b12", name="b2" ) # Create Algebra for expected Mean Matrices meanG <- mxMatrix( type="Full", nrow=1, ncol=ntv, free=TRUE, values=svMe, labels="xbmi", name="meanG" ) expMean <- mxAlgebra( expression= meanG + cbind(b1%*%Age,b1%*%Age)+b2%*%Sex, name="expMean" ) # Create Matrices for Path Coefficients pathA <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPa, label="a11", lbound=lbPa, name="a" ) pathC <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPa, label="c11", lbound=lbPa, name="c" ) pathE <- mxMatrix( type="Lower", nrow=nv, ncol=nv, free=TRUE, values=svPe, label="e11", lbound=lbPa, 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 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(pathB1,pathB2, meanG, pathA, pathC, pathE, covA, covC, covE, covP) defs <- list(defAge, defSex) 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" ) # Build Model with Confidence Intervals modelACE <- mxModel( "oneACEca", pars, modelMZ, modelDZ, multi, estVC, ciACE ) # ------------------------------------------------------------------------------ # RUN MODEL # Run ACE Model fitACE <- mxTryHard( modelACE, intervals=T, extraTries = 10, finetuneGradient = T, exhaustive = T ) sumACE <- summary( fitACE ) summary(fitACE) # Compare with Saturated Model mxCompare( fit, fitACE ) #lrtSAT(fitACE,-2ll,df) # Print Goodness-of-fit Statistics & Parameter Estimates fitGofs(fitACE) fitEsts(fitACE) # ------------------------------------------------------------------------------ # RUN SUBMODELS # Test Significance of Covariate modelCov <- mxModel( fitACE, name="testCov" ) modelCov <- omxSetParameters( modelCov, label=c("b11","b12"), free=FALSE, values=0 ) fitCov <- mxRun( modelCov ) mxCompare( fitACE, fitCov ) # Run AE model modelAE <- mxModel( fitACE, name="oneAEca" ) modelAE <- omxSetParameters( modelAE, labels="c11", free=FALSE, values=0 ) fitAE <- mxRun( modelAE, intervals=T ) mxCompare( fitACE, fitAE ) fitGofs(fitAE) fitEsts(fitAE) summary(fitAE) # Run CE model modelCE <- mxModel( fitACE, name="oneCEca" ) modelCE <- omxSetParameters( modelCE, labels="a11", free=FALSE, values=0 ) fitCE <- mxRun( modelCE, intervals=T ) mxCompare( fitACE, fitCE ) fitGofs(fitCE) fitEsts(fitCE) # Run E model modelE <- mxModel( fitAE, name="oneEca" ) modelE <- omxSetParameters( modelE, labels="a11", free=FALSE, values=0 ) fitE <- mxRun( modelE, intervals=T ) mxCompare( fitAE, fitE ) fitGofs(fitE) fitEsts(fitE) # Print Comparative Fit Statistics mxCompare( fitACE, nested <- list(fitCov, fitAE, fitCE, fitE) ) round(rbind(fitACE$VC$result,fitAE$VC$result,fitCE$VC$result,fitE$VC$result),4)
Thank you so much.