This function will return a vector, with the same length as the number of rows of the provided data frame, corresponding to the average mahalanobis distances of each row from the whole data set.
Arguments
- data
A data frame
- keep.NA
Ensure that every row with missing data remains NA in the output? TRUE by default.
- robust
Attempt to compute mahalanobis distance based on robust covariance matrix? FALSE by default
- stringsAsFactors
Convert non-factor string columns into factors? FALSE by default
Details
This is useful for finding anomalous observations, row-wise.
It will convert any categorical variables in the data frame into numerics
as long as they are factors. For example, in order for a character
column to be used as a component in the distance calculations, it must
either be a factor, or converted to a factor by using the
stringsAsFactors parameter.
Examples
maha_dist(mtcars)
#> [1] 8.946673 8.287933 8.937150 6.096726 5.429061 8.877558 9.136276
#> [8] 10.030345 22.593116 12.393107 11.058878 9.476126 5.594527 6.026462
#> [15] 11.201310 8.672093 12.257618 9.078630 14.954377 10.296463 13.432391
#> [22] 6.227235 5.786691 11.681526 6.718085 3.645789 18.356164 14.000669
#> [29] 21.573003 11.152850 19.192384 9.888781
maha_dist(iris, robust=TRUE)
#> [1] 7.181303 14.209332 9.233412 14.029571 6.547538 9.032628 9.557745
#> [8] 9.434082 16.327924 14.609934 7.944119 12.241370 14.152604 10.634152
#> [15] 8.940285 9.311006 7.996840 7.676170 10.686693 6.772707 14.171183
#> [22] 8.199367 4.819010 17.927827 21.970559 17.509804 12.134829 8.718618
#> [29] 8.664029 14.539774 15.467206 13.918214 12.751421 7.800228 13.513486
#> [36] 9.613279 9.535921 8.718478 13.079168 9.504772 7.041884 36.721567
#> [43] 10.581647 18.598838 14.208248 15.428511 9.871731 10.898606 7.630712
#> [50] 9.183846 12.142542 5.700266 15.153194 9.430086 12.108781 11.172977
#> [57] 9.444704 5.395403 11.484362 7.646899 10.479824 5.254603 14.996254
#> [64] 11.744912 2.849079 6.677997 10.917922 9.978773 24.822657 4.694515
#> [71] 20.355909 3.146007 22.559940 18.395726 5.389387 6.864585 17.525227
#> [78] 19.539023 8.151810 3.098777 4.944060 5.362978 2.057880 26.155842
#> [85] 14.053245 8.768882 10.215374 17.593198 3.270086 5.936323 13.524049
#> [92] 8.625081 3.824912 5.800611 5.591581 5.707430 3.987159 4.389560
#> [99] 9.471902 3.068005 30.970783 32.288310 16.382188 27.303966 18.252965
#> [106] 14.211336 57.834820 27.316610 17.104499 29.976800 42.865459 24.851409
#> [113] 25.740563 34.263970 56.920919 43.839091 29.769508 30.314755 8.766718
#> [120] 35.514678 27.338244 44.696221 18.955613 38.729456 26.599257 32.185485
#> [127] 44.286331 45.304056 18.655092 42.634687 19.136274 40.640072 20.651593
#> [134] 46.870404 53.923075 20.573812 41.472972 33.431351 49.320574 31.907998
#> [141] 34.144669 57.844485 32.288310 20.731382 39.082779 47.916444 32.925457
#> [148] 34.182320 45.599451 41.065734
library(magrittr) # for piping operator
library(dplyr) # for "everything()" function
# using every column from mtcars, compute mahalanobis distance
# for each observation, and ensure that each distance is within 10
# median absolute deviations from the median
mtcars %>%
insist_rows(maha_dist, within_n_mads(10), everything())
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## anything here will run