jamovi

Code: Select all

library(jmv)
library(dplyr)
library(magrittr)

dat <- select(data, "V1", "V2", "V3", "V4") # select only the variables you want to use 

Sx <- cov(dat) # get the covariance matrix 

D2 <- mahalanobis(dat, colMeans(dat), Sx) # calculate the Mahalnobis distances on the centred data

# Optionally, make some fancy plots 
plot(density(D2, bw = 0.5),
     main="Squared Mahalanobis distances, n=100, p=3") ; rug(D2)
qqplot(qchisq(ppoints(100), df = 3), D2,
       main = expression("Q-Q plot of Mahalanobis" * ~D^2 *
                         " vs. quantiles of" * ~ chi[3]^2))
abline(0, 1, col = 'gray')

# Add the distances to your selected dataset and calculate whether there are outliers greater than 4.5

dat$mahalanobis <-D2
dat$outlier <- FALSE
dat$outlier[dat$mahalanobis > 4.5] <- TRUE

dat