outliers_mcd: MCD function to detect outliers

In mdelacre/Routliers: Robust Outliers Detection

Description

Detecting multivariate outliers using the Minimum Covariance Determinant approach

Usage

outliers_mcd(x, h, alpha, na.rm)

Arguments

x	matrix of bivariate values from which we want to compute outliers
h	proportion of dataset to use in order to compute sample means and covariances
alpha	nominal type I error probability (by default .01)
na.rm	set whether Missing Values should be excluded (na.rm = TRUE) or not (na.rm = FALSE) - defaults to TRUE

Value

Returns Call, Max distance, number of outliers

Examples

#### Run outliers_mcd
# The default is to use 75% of the datasets in order to compute sample means and covariances
# This proportion equals 1-breakdown points (i.e. h = .75 <--> breakdown points = .25)
# This breakdown points is encouraged by Leys et al. (2018)
data(Attacks)
SOC <- rowMeans(Attacks[,c("soc1r","soc2r","soc3r","soc4","soc5","soc6","soc7r",
"soc8","soc9","soc10r","soc11","soc12","soc13")])
HSC <- rowMeans(Attacks[,22:46])
res <- outliers_mcd(x = cbind(SOC,HSC), h = .75)
res

# Moreover, a list of elements can be extracted from the function,
# such as the position of outliers in the dataset
# and the coordinates of outliers
res$outliers_pos
res$outliers_val

mdelacre/Routliers documentation built on Oct. 10, 2020, 5:02 a.m.