plot_outliers_mahalanobis {Routliers}

Plotting function for the Mahalanobis distance approach

Description

plotting data and highlighting multivariate outliers detected with the mahalanobis distance approach

Usage

plot_outliers_mahalanobis(res, x, pos_display = FALSE)

Arguments

res

result of the outliers_mad function from which we want to create a plot

x

matrix of multivariate values from which we want to compute outliers. Last column of the matrix is considered as the DV in the regression line.

pos_display

set whether the position of outliers in the dataset should be displayed on the graph (pos_display = TRUE) or not (pos_display = FALSE)

Details

plotting data and highlighting multivariate outliers detected with the MCD function Additionnally, the plot return two regression lines: the first one including all data and the second one including all observations but the detected outliers. It allows to observe how much the outliers influence of outliers on the regression line.

Value

None

Examples

#### Run plot_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))
plot_outliers_mahalanobis(res, x = cbind(SOC,HSC))
# it's also possible to display the position of the multivariate outliers ion the graph
# preferably, when the number of multivariate outliers is not too high
c1 <- c(1,4,3,6,5,2,1,3,2,4,7,3,6,3,4,6)
c2 <- c(1,3,4,6,5,7,1,4,3,7,50,8,8,15,10,6)
res2 <- outliers_mahalanobis(x = cbind(c1,c2))
plot_outliers_mahalanobis(res2, x = cbind(c1,c2),pos_display = TRUE)

# When no outliers are detected, only one regression line is displayed
c3 <- c(1,4,3,6,5)
c4 <- c(1,3,4,6,5)
res3 <- outliers_mahalanobis(x = cbind(c3,c4))
plot_outliers_mahalanobis(res3,x = cbind(c3,c4))