Nothing
"rg.robmva" <-
function(x, proc = "mcd", wts = NULL, main = deparse(substitute(x)))
{
# Procedure to undertake robust multivariate analyses using the minimum
# volume ellipsoid (MVE), minimim covariance determinant (MCD) procedure,
# or a user supplied set of 0-1 weights, wts, to identify a set of outliers to omit
# from estimation of geochemical background parameters. Note: The
# cov.mcd procedure is limited to a maximum of 50 variables. Both of these
# procedures lead to a vector of 0-1 wts. Of the two procedures, mcd is
# recommended and is the default.
#
# Note this procedure uses svd() rather than the classic solve().
#
# Provision is made for the user to provide a set of n weights via wts,
# e.g., wts = c(1,1,0,1,0,1,...........1,1). Such a set of weights can be
# generated by using the Graphical Adaptive Interactive Trimming (GAIT)
# procedure available though rg.md.gait().
#
# Using 0-1 wts the parameters of the background distribution are estimated
# by cov.wt(). Following this a robust R-mode PCA is undertaken, and robust
# Mahalanobis distances are estimated for the total data set. The estimation
# of Mahalanobis distances is only undertaken if x is non-singular, i.e., the
# lowest eigenvalue is > 10e-4.
#
# PCA output may be plotted with rg.rqpca.plot() and rg.rqpca.screeplot(), and
# Mahalanobis distances may be plotted with rg.md.plot(). The PCA solution
# may be rotated using rg.rotate().
#
#Determine the length of the vectors
if(!is.matrix(x)) stop("Not a Matrix")
n <- length(x[, 1])
p <- length(x[1, ])
matnames <- dimnames(x)
#if(n <= 3 * p)
# cat("*** Proceed With Great Care, n < 3p ***\n")
if(is.null(wts)) {
#Use mve or mcd procedure to identify core background subset
if(p > 50) proc <- "mve"
if(proc == "mve") {
save <- cov.mve(x,cor=TRUE)
wts <- save$mve.wt
}
else {
save <- covMcd(x,cor=TRUE)
wts <- save$mcd.wt
}
}
else {
proc == "wts"
#Use cov.wt() to estimate parameters charcterizing the core (background)
save <- cov.wt(x, wt = wts, cor = TRUE)
}
nc <- sum(wts)
#cat(" n = ", n, "\tnc = ", nc, "\tp = ", p, "\t\tnc/p = ", round(nc/p, 2), "\n")
#if(nc <= 5 * p)
# cat("*** Proceed with Care, nc is < 5p ***\n")
#if(nc <= 3 * p)
# cat("*** Proceed With Great Care, nc = ", nc, ", which is < 3p ***\n")
#Standardize whole data set with robust background estimators
temp <- sweep(x, 2, save$center, "-")
sd <- sqrt(diag(save$cov))
snd <- sweep(temp, 2, sd, "/")
#Standardize for RQ PCA and compute
# w <- sweep(snd, 2, sqrt(n), "/")
# wt <- t(as.matrix(w))
# a <- wt %*% as.matrix(w)
b <- svd(save$cor)
sumc <- sum(b$d)
econtrib <- 100 * (b$d/sumc)
rqscore <- snd %*% b$v
### vcontrib <- colVars(rqscore)
vcontrib <- apply(rqscore,2,var)
sumv <- sum(vcontrib)
pvcontrib <- (100 * vcontrib)/sumv
cpvcontrib <- cumsum(pvcontrib)
b1 <- b$v * 0
diag(b1) <- sqrt(b$d)
rload <- b$v %*% b1
rcr <- rload[, ] * 0
rcr1 <- apply(rload^2, 1, sum)
rcr <- 100 * sweep(rload^2, 1, rcr1, "/")
#test for non-singularity and compute robust Mahalanobis distances
if(b$d[p] > 10^-4) {
md <- mahalanobis(x, save$center, save$cov)
temp <- (nc - p)/(p * (nc + 1))
ppm <- 1 - pf(temp * md, p, nc - p)
epm <- 1 - pchisq(md, p)
}
else {
#cat(" Lowest eigenvalue > 10^-4, Mahalanobis distances not computed\n")
md <- NULL
ppm <- NULL
epm <- NULL
}
invisible(list(main = main, input = deparse(substitute(x)), proc = proc, n = n, nc = nc,
p = p, matnames = matnames, wts = wts, mean = save$center, cov = save$cov, sd =
sd, snd = snd, r = save$cor, eigenvalues = b$d, econtrib = econtrib,
eigenvectors = b$v, rload = rload, rcr = rcr, rqscore = rqscore, vcontrib =
vcontrib, pvcontrib = pvcontrib, cpvcontrib = cpvcontrib, md = md, ppm = ppm,
epm = epm, nr = NULL))
}
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