emxCholeskyComponent: Creates component for a Biometric Cholesky Model

In EasyMx: Easy Model-Builder Functions for 'OpenMx'

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)

EasyMx documentation built on Feb. 16, 2023, 7:27 p.m.