Description Usage Arguments Value Examples
Detecting multivariate outliers using the Mahalanobis distance
1 | outliers_mahalanobis(x, alpha, na.rm)
|
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 |
Returns Call, Max distance, number of outliers
1 2 3 4 5 6 7 8 9 10 11 | #### 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
|
[1] 5 105 235 365 408 635 640 648 667 679 718 779 1147 1245 1259
[16] 1260 1401 1523 1705 1908 1910 1911 1937 1938 2036 2073
X1 X2
1 5.846154 3.04
2 4.384615 3.32
3 4.538462 3.44
4 4.230769 3.32
5 3.307692 1.16
6 4.307692 3.28
7 3.692308 3.68
8 3.846154 3.48
9 3.538462 3.96
10 2.384615 3.88
11 6.384615 2.24
12 3.769231 3.56
13 4.307692 3.44
14 4.307692 3.32
15 4.846154 3.28
16 4.846154 3.28
17 3.461538 1.00
18 5.923077 2.60
19 3.153846 3.64
20 1.615385 2.44
21 2.307692 1.76
22 2.000000 2.52
23 1.769231 2.56
24 1.692308 3.08
25 3.307692 3.56
26 1.000000 3.52
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