parody (version 1.30.0)

mv.calout.detect: calibrated multivariate outlier detection

Description

interface to a parametric multivariate outlier detection algorithm

Usage

mv.calout.detect(x, k = min(floor((nrow(x) - 1)/2), 100), Ci = C.unstr,  lamfun = lams.unstr, alpha = 0.05, method = c("parametric",  "rocke", "kosinski.raw", "kosinski.exch")[1], ...)

Arguments

x

data matrix

k

upper bound on number of outliers; defaults to just less than half the sample size

Ci

function computing Ci, the covariance determinant ratio excluding row i. At present, sole option is C.unstr (Caroni and Prescott 1992 Appl Stat).

lamfun

function computing lambda, the critical values for Ci

alpha

false outlier labeling rate

method

string identifying algorithm to use

...

reserved for future use

Value

a list with components

inds

indices of outlying rows

vals

values of outlying rows

k

input parameter k

alpha

input parameter alpha

Details

bushfire is a dataset distributed by Kosinski to illustrate his method.

References

Examples

data(tcost)
mv.calout.detect(tcost)
data(bushfire)
mv.calout.detect(bushfire)