Nothing
setClass("icsOut", representation(outliers = "integer",
ics.distances = "numeric",
ics.dist.cutoff = "numeric",
level.dist = "numeric",
level.test = "numeric",
method = "character",
index = "numeric",
test = "character",
criterion = "numeric",
adjust = "logical",
type = "character",
mDist = "integer",
mEig = "integer",
S1name = "character",
S2name = "character"))
setMethod("show", signature(object = "icsOut"),
function(object)
{
comps <- object@index
if (sum(comps)<0.5) {
ncomps <- 0
print(paste(ncomps, " components were selected and no outliers were detected.", sep = ""))
} else {
ncomps <- length(comps)
print(paste(ncomps, " components were selected and ", sum(object@outliers), " outliers were detected.", sep = ""))
}
#invisible(object@outliers)
}
)
setMethod("summary", signature(object = "icsOut"),
function(object, digits = 4)
{
comps <- object@index
if (sum(comps)<0.5) ncomps <- 0 else ncomps <- length(comps)
if (object@method == "norm.test") METHOD <- paste(object@method, " (", object@test, ")", sep = "") else METHOD <- object@method
cat("\nICS based on two scatter matrices and two location estimates\n")
cat("S1: ", object@S1name)
cat("\nS2: ", object@S2name)
cat("\n")
cat("\nSearching for a small proportion of outliers\n")
cat("\n")
#cat(paste("Components selected: ", ncomps, sep = "") )
#cat("\n")
cat(paste("Components selected at nominal level ", object@level.test, ": ", ncomps, sep = "") )
cat("\n")
cat(paste("Selection method: ", METHOD, sep = ""))
cat("\n")
#cat(paste("Number of outliers: ", sum(object@outliers), sep = ""))
cat(paste("Number of outliers at nominal level ", object@level.dist, ": ", sum(object@outliers), sep = ""))
cat("\n")
invisible(object)
}
)
#setMethod("plot", signature(x = "icsOut", y = "missing"),
#function(x, pch.out = 16, pch.good = 4, col.out = 1, col.good = grey(0.5), col.cut = 1,
# lwd.cut = 1, lty.cut = 1, xlab = "Observation Number", ylab = "ICS distances", ...)
# {
# colPoints <- ifelse(x@outliers == 1L, col.out, col.good)
# pchPoints <- ifelse(x@outliers == 1L, pch.out, pch.good)
#
# plot(x@ics.distances, col = colPoints, pch = pchPoints, xlab = xlab, ylab = ylab, ...)
# abline(h = x@ics.dist.cutoff, col = col.cut, lwd = lwd.cut, lty = lty.cut)
# }
#)
setMethod("plot", signature(x = "icsOut", y = "missing"),
function(x, pch.out = 16, pch.good = 4, col.out = 1, col.good = grey(0.5), col.cut = 1,
lwd.cut = 1, lty.cut = 1, xlab = "Observation Number", ylab = "ICS distances", ...)
{
YESylim <- hasArg("ylim")
if (sum(x@outliers)>0.5){
colPoints <- ifelse(x@outliers == 1L, col.out, col.good)
pchPoints <- ifelse(x@outliers == 1L, pch.out, pch.good)
plot(x@ics.distances, col = colPoints, pch = pchPoints, xlab = xlab, ylab = ylab, ...)
abline(h = x@ics.dist.cutoff, col = col.cut, lwd = lwd.cut, lty = lty.cut)
} else {
if (sum(x@ics.distances) > 0){
if (!YESylim) {
plot(x@ics.distances, col = col.good, pch = pch.good, xlab = xlab, ylab = ylab, ylim = c(0, x@ics.dist.cutoff), ...)
} else {
plot(x@ics.distances, col = col.good, pch = pch.good, xlab = xlab, ylab = ylab, ...)
}
abline(h = x@ics.dist.cutoff, col = col.cut, lwd = lwd.cut, lty = lty.cut)
} else {
plot(x@ics.distances, xlab = xlab, ylab = ylab, type = "n", ...)
text(length(x@ics.distances)/2, 0, labels = c("No components have been selected for outlier detection.\n There is nothing to plot."))
}
}
}
)
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