Computes mahalanobis distance for each row of data frame

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.

Usage

maha_dist(data, keep.NA = TRUE, robust = FALSE, stringsAsFactors = FALSE)

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

Value

A vector of observation-wise mahalanobis distances.

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.

See also

insist_rows

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