function (x, cor = FALSE, scores = TRUE, covmat = NULL, subset = rep(TRUE,
nrow(as.matrix(x))), ...)
{
cl <- match.call()
cl[[1L]] <- as.name("princomp")
if (!missing(x) && !missing(covmat))
warning("both 'x' and 'covmat' were supplied: 'x' will be ignored")
z <- if (!missing(x))
as.matrix(x)[subset, , drop = FALSE]
if (is.list(covmat)) {
if (any(is.na(match(c("cov", "n.obs"), names(covmat)))))
stop("'covmat' is not a valid covariance list")
cv <- covmat$cov
n.obs <- covmat$n.obs
cen <- covmat$center
}
else if (is.matrix(covmat)) {
cv <- covmat
n.obs <- NA
cen <- NULL
}
else if (is.null(covmat)) {
dn <- dim(z)
if (dn[1L] < dn[2L])
stop("'princomp' can only be used with more units than variables")
covmat <- cov.wt(z)
n.obs <- covmat$n.obs
cv <- covmat$cov * (1 - 1/n.obs)
cen <- covmat$center
}
else stop("'covmat' is of unknown type")
if (!is.numeric(cv))
stop("PCA applies only to numerical variables")
if (cor) {
sds <- sqrt(diag(cv))
if (any(sds == 0))
stop("cannot use cor=TRUE with a constant variable")
cv <- cv/(sds %o% sds)
}
edc <- eigen(cv, symmetric = TRUE)
ev <- edc$values
if (any(neg <- ev < 0)) {
if (any(ev[neg] < -9 * .Machine$double.eps * ev[1L]))
stop("covariance matrix is not non-negative definite")
else ev[neg] <- 0
}
cn <- paste("Comp.", 1L:ncol(cv), sep = "")
names(ev) <- cn
dimnames(edc$vectors) <- if (missing(x))
list(dimnames(cv)[[2L]], cn)
else list(dimnames(x)[[2L]], cn)
sdev <- sqrt(ev)
sc <- if (cor)
sds
else rep(1, ncol(cv))
names(sc) <- colnames(cv)
scr <- if (scores && !missing(x) && !is.null(cen))
scale(z, center = cen, scale = sc) %*% edc$vectors
if (is.null(cen))
cen <- rep(NA_real_, nrow(cv))
edc <- list(sdev = sdev, loadings = structure(edc$vectors,
class = "loadings"), center = cen, scale = sc, n.obs = n.obs,
scores = scr, call = cl)
class(edc) <- "princomp"
edc
}
<environment: namespace:stats>