outliers_mahalanobis: mahalanobis function to detect outliers

In mdelacre/Routliers: Robust Outliers Detection

Description

Detecting multivariate outliers using the Mahalanobis distance

Usage

outliers_mahalanobis(x, alpha, na.rm)

Arguments

x	matrix of bivariate values from which we want to compute outliers
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_mahalanobis
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_mahalanobis(x = cbind(SOC,HSC), na.rm = TRUE)
# 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.