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# A function to create a correlation matrix with a hierarchical structure
"make.hierarchical" <-
function (gload=NULL,fload=NULL,n=0,raw=FALSE) {
# require(MASS)
if(is.null(gload)) gload=matrix(c(.9,.8,.7),nrow=3)
if(is.null(fload)) {fload <-matrix(c(
.8,0,0,
.7,0,.0,
.6,0,.0,
0,.7,.0,
0,.6,.0,
0,.5,0,
0,0,.6,
0,0,.5,
0,0,.4), ncol=3,byrow=TRUE)}
fcor <- gload %*% t(gload) #the factor correlation matrix
diag(fcor) <-1 #put ones on the diagonal
model <- fload %*% fcor %*% t(fload) #the model correlation matrix for oblique factors
diag(model)<- 1 # put ones along the diagonal
nvar <- dim(fload)[1]
colnames(model) <- rownames(model) <- paste("V",1:nvar,sep="")
if(n>0) {
# mu <- rep(0,nvar)
#model <- mvrnorm(n = n, mu, Sigma=model, tol = 1e-6, empirical = FALSE)
#the next 3 lines replaces mvrnorm (adapted from mvrnorm, but without the checks)
eX <- eigen(model)
model <- matrix(rnorm(nvar * n),n)
model <- t( eX$vectors %*% diag(sqrt(pmax(eX$values, 0)), nvar) %*% t(model))
if (!raw ) { model <- cor(model) } }
make.hierarchical <- model }
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