Description Usage Arguments Value Examples
Detecting multivariate outliers using the Minimum Covariance Determinant approach
1 | outliers_mcd(x, h, alpha, na.rm)
|
x |
matrix of bivariate values from which we want to compute outliers |
h |
proportion of dataset to use in order to compute sample means and covariances |
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 12 13 14 15 16 | #### Run outliers_mcd
# The default is to use 75% of the datasets in order to compute sample means and covariances
# This proportion equals 1-breakdown points (i.e. h = .75 <--> breakdown points = .25)
# This breakdown points is encouraged by Leys et al. (2018)
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_mcd(x = cbind(SOC,HSC), h = .75)
res
# Moreover, 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
|
Call:
outliers_mcd.default(x = cbind(SOC, HSC), h = 0.75)
Limit distance of acceptable values from the centroid :
[1] 9.21034
Number of detected outliers:
total
54
[1] 5 98 105 126 166 234 235 241 287 327 365 408 452 469 513
[16] 544 586 635 640 648 667 679 718 727 779 1147 1186 1242 1245 1250
[31] 1259 1260 1401 1504 1505 1509 1523 1687 1705 1757 1815 1891 1908 1910 1911
[46] 1912 1913 1923 1937 1938 1992 2021 2036 2073
X1 X2
1 5.846154 3.04
2 4.461538 3.04
3 4.384615 3.32
4 4.461538 3.20
5 3.384615 3.36
6 4.230769 3.24
7 4.538462 3.44
8 4.000000 3.32
9 2.615385 3.40
10 2.538462 3.32
11 4.230769 3.32
12 3.307692 1.16
13 4.461538 3.08
14 4.153846 3.20
15 3.538462 3.28
16 2.461538 3.40
17 3.230769 3.48
18 4.307692 3.28
19 3.692308 3.68
20 3.846154 3.48
21 3.538462 3.96
22 2.384615 3.88
23 6.384615 2.24
24 6.538462 1.92
25 3.769231 3.56
26 4.307692 3.44
27 2.000000 2.72
28 3.615385 3.32
29 4.307692 3.32
30 2.076923 2.92
31 4.846154 3.28
32 4.846154 3.28
33 3.461538 1.00
34 2.076923 3.20
35 2.461538 3.28
36 2.153846 3.32
37 5.923077 2.60
38 2.461538 3.52
39 3.153846 3.64
40 4.769231 3.04
41 3.461538 3.28
42 2.230769 3.36
43 1.615385 2.44
44 2.307692 1.76
45 2.000000 2.52
46 2.000000 3.12
47 2.230769 3.16
48 2.769231 3.32
49 1.769231 2.56
50 1.692308 3.08
51 2.307692 2.16
52 3.307692 3.36
53 3.307692 3.56
54 1.000000 3.52
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