mv.calout.detect: calibrated multivariate outlier detection

In vjcitn/parodyGHA: Parametric And Resistant Outlier DYtection

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

Details

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

Value

a list with components

inds	indices of outlying rows
vals	values of outlying rows
k	input parameter k
alpha	input parameter alpha

Author(s)

VJ Carey

References

C. Caroni and P. Prescott, Journal of the Royal Statistical Society. Series C (Applied Statistics), Vol. 41, No. 2 (1992), pp. 355-364

Examples

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

vjcitn/parodyGHA documentation built on Dec. 23, 2021, 4:06 p.m.