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outlier <- function(x, ...) UseMethod("outlier")
outlier.randomForest <- function(x, ...) {
if (!inherits(x, "randomForest")) stop("x is not a randomForest object")
if (x$type == "regression") stop("no outlier measure for regression")
if (is.null(x$proximity)) stop("no proximity measures available")
outlier.default(x$proximity, x$y)
}
outlier.default <- function(x, cls=NULL, ...) {
if (nrow(x) != ncol(x)) stop ("x must be a square matrix")
n <- nrow(x)
if (is.null(cls)) cls <- rep(1, n)
cls <- factor(cls)
lvl <- levels(cls)
cls.n <- table(cls)[lvl]
id <- if (is.null(rownames(x))) 1:n else rownames(x)
outlier <- structure(rep(NA, n), names=id)
for (i in lvl) {
iclass <- cls == i
out <- rowSums(x[iclass, iclass]^2)
out <- n / ifelse(out == 0, 1, out)
out <- (out - median(out)) / mad(out)
outlier[iclass] <- out
}
outlier
}
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