Nothing
"glb.fa" <-
function(r,key= NULL){
cl <- match.call() #for eventual fancy printing with the call listed
nvar <- dim(r)[2] #find a correlation matrix if using a data matrix
if(dim(r)[1] != dim(r)[2]) {r <- cor(r,use="pairwise")} else {
if(!is.matrix(r)) r <- as.matrix(r)
r <- cov2cor(r)} #make sure it is a correlation matrix not a covariance or data matrix
if(is.null(colnames(r))) { rownames(r) <- colnames(r) <- paste("V",1:nvar,sep="") }
if (!is.null(key)) { key <- as.vector(key)
r <- diag(key) %*% r %*% diag(key)
flip <- FALSE #we do this if we specify the key
} else {key <- rep(1,nvar) }
nv <- dim(r)[1] #how many variables
f1 <- fa(r) #factor it #first find the eigen values of the factor model
nf <-length(which(f1$values > 0)) #how many are real
df <- nv * (nv-1)/2 - nf*nv + nf*(nf-1)/2 #check for degrees of freedom
if (df < 0 ) nf <- nf-1
fn <- fa(r,nf,rotate="none")
rr <- r
diag(rr) <- fn$communality #based upon nf factors
glb <- sum(rr)/sum(r)
return(list(glb=glb,communality = fn$communality,numf = nf,Call=cl))
}
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