spca:

In musto101/wilcox_R: What the package does (short line)

Usage

spca(x, var.fun = pbvar, SCORES = FALSE, pval = 1)

Arguments

x
var.fun
SCORES
pval

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (x, var.fun = pbvar, SCORES = FALSE, pval = 1) 
{
    scores <- NULL
    mvec <- spat(x)
    n <- nrow(x)
    m <- ncol(x)
    y <- matrix(ncol = m, nrow = n)
    xdif <- matrix(ncol = m, nrow = n)
    for (i in 1:n) {
        y[i, ] <- x[i, ] - mvec
        xdif[i, ] <- y[i, ]
        y[i, ] <- y[i, ]/sqrt(sum(y[i, ]^2))
    }
    e.val <- eigen(var(y))
    b <- e.val$vectors
    lam <- NA
    for (j in 1:ncol(x)) {
        val <- NA
        for (i in 1:n) val[i] <- sum(b[, j] * x[i, ])
        lam[j] <- var.fun(val)
    }
    if (SCORES) {
        ord.val <- order(lam)
        mn1 <- m - pval + 1
        Bp <- e.val$vectors[, ord.val[mn1:m]]
        scores <- matrix(ncol = pval, nrow = n)
        for (i in 1:n) scores[i, ] <- t(as.matrix(Bp)) %*% as.matrix(xdif[i, 
            ])
    }
    list(eigen.val = lam, B = b, spat.mu = mvec, scores = scores)
  }

musto101/wilcox_R documentation built on May 23, 2019, 10:52 a.m.