#1. create data x = rnorm(1000, 0, 4) y = rnorm(1000, 0, 1) #2. set the outlier value y[1] = 17 #3. normalize the data x_norm = (x - min(x))/(max(x)-min(x)) y_norm = (y - min(y))/(max(y)-min(y)) #4. calculate euclidean distance of normalized data meanx_norm = mean(x_norm) meany_norm = mean(y_norm) eucliddist_norm = sqrt((x_norm-meanx_norm)*(x_norm-meanx_norm) + (y_norm-meany_norm)*(y_norm-meany_norm)) #5. calculate mahalanobis distance of normalized data data_norm = cbind(x_norm,y_norm) datamean_norm = cbind(meanx_norm, meany_norm) datacov_norm = cov(data_norm) mahaldist_norm = mahalanobis(data_norm, datamean_norm, datacov_norm) #6. Exportieren der Daten im csv Format exportdata = cbind(x_norm, y_norm, eucliddist_norm, mahaldist_norm) write.table(exportdata, "mahalanobis2.csv", row.names=FALSE, col.names=c("x_norm", "y_norm", "eucliddist_norm", "mahaldist_norm"), sep=";")