getOutliers: Compute multidimensional outliers on a numeric matrix

In luisfo/muod.outliers:

Description

Compute multidimensional outliers on a numeric matrix

Usage

getOutliers(data, n = ncol(data), nGroups = n/getOption("mc.cores", 1),
  method = c("rcpp", "classic", "normal", "old"), parClus,
  outl.method = c("deriv.old", "deriv-enh", "deriv", "roc", "tangent"),
  slope = 2, benchmark = c(1, 0, 1), plotCutoff = F)

Arguments

data	A numeric matrix to detect outliers on its columns
method	The method to compute the MUOD indices
outl.method	The method to choose the outliers among the indices
slope	The slope threshold when outl.method = "deriv*"

Value

A list of outliers, namely shape, magnitude, amplitude

Examples

data(mtcars)

my.data <- as.matrix(mtcars)
outliers <- getOutliers(t(my.data), slope=1)
outliers

luisfo/muod.outliers documentation built on May 21, 2019, 1:19 p.m.