emxCholeskyComponent {EasyMx}

Creates component for a Biometric Cholesky Model

Description

This function creates all the objects needed for a Cholesky component.

Usage

emxCholeskyComponent(x, xname, xvalues, xfree,
                     h=2, hname=paste0('H', xname), hvalues, hlabels)

Arguments

x

character vector. The base names of the variables used for the matrix with no repetition for twins (x, y, z not x1, y1, z1, x2, y2, z2).

xname

character. Name of the component matrix.

xvalues

numeric vector. Values of the component matrix.

xfree

logical vector. Whether each element of the component matrix is freely estimates.

h

numeric. The number of variables for the relatedness matrix, i.e. the number of critters with relationships

hname

character. Name of the relatedness matrix.

hvalues

numeric vector. Values for the relatedness matrix.

hlabels

character vector. Labels for the relatedness matrix.

Details

This function is a combination of emxCholeskyVariance, emxRelatednessMatrix, and emxKroneckerVariance.

Value

A list with elements (1) the lower triangular matrix for the Cholesky, (2) the full positive definite variance matrix, (3) the relatedness matrix, and (4) the Kronecker product of the variance matrix and the relatedness matrix.

See Also

emxGeneticFactorComponent

Examples

   
# Create an ACE model in 22 lines
require(EasyMx)
require(OpenMx)
data(twinData)
twinVar = names(twinData)
selVars <- c('ht1', 'bmi1','ht2','bmi2')
mzdzData <- subset(twinData, zyg %in% c(1, 3), c(selVars, 'zyg'))
mzdzData$RCoef <- c(1, NA, .5)[mzdzData$zyg]
nVar = length(selVars)/2
x <- paste0('x', 1:nVar)

acomp <- emxCholeskyComponent(x, 'A', hvalues=c(1, .5, 1), hlabels=c(NA, 'data.RCoef', NA))
ccomp <- emxCholeskyComponent(x, 'C', hvalues=c(1, 1, 1))
ecomp <- emxCholeskyComponent(x, 'E', hvalues=c(1, 0, 1))
totalVar <- mxAlgebra(AKron + CKron + EKron, 'V', dimnames=list(selVars, selVars))
totalMean <- emxMeans(selVars, type='twin')
expect <- mxExpectationNormal(totalVar$name, totalMean$name)
fitfun <- mxFitFunctionML()

comlist <- c(acomp, ccomp, ecomp, list(totalVar, totalMean, expect, fitfun))

model <- mxModel('model', comlist, mxData(mzdzData, 'raw'))
## Not run: 
run2 <- mxRun(model)

## End(Not run)