R/pcaeigenk.R

In mlesnoff/rchemo: Dimension reduction, Regression and Discrimination for Chemometrics

pcaeigenk <- function(X, weights = NULL, nlv) {
    X <- .mat(X)
    zdim <- dim(X)
    n <- zdim[1]
    p <- zdim[2]
    nlv <- min(nlv, n, p)
    if(is.null(weights))
        weights <- rep(1, n)
    weights <- .mweights(weights)
    xmeans <- .colmeans(X, weights = weights)
    X <- .center(X, xmeans)
    zX <- sqrt(weights) * X
    res <- eigen(tcrossprod(zX), symmetric = TRUE)
    eig <- res$values[seq_len(min(n, p))]
    eig[eig < 0] <- 0
    sv <- sqrt(eig)
    P <- crossprod(zX, .scale(res$vectors[, seq_len(nlv), drop = FALSE], 
                              scale = sv[seq_len(nlv)]))
    T <- X %*% P
    row.names(T) <- row.names(X)
    row.names(P) <- colnames(X)
    colnames(T) <- colnames(P) <-  paste("pc", seq_len(nlv), sep = "")
    structure(
        list(T = T, P = P, sv = sv, eig = eig,
            xmeans = xmeans, weights = weights, niter = NULL, conv = NULL),
        class = c("Pca"))
}