A permutation sample is the same size as the original data set and is made
by permuting/shuffling one or more columns. This results in analysis
samples where some columns are in their original order and some columns
are permuted to a random order. Unlike other sampling functions in
rsample, there is no assessment set and calling assessment() on a
permutation split will throw an error.
Arguments
- data
A data frame.
- permute
One or more columns to shuffle. This argument supports
tidyselectselectors. Multiple expressions can be combined withc(). Variable names can be used as if they were positions in the data frame, so expressions likex:ycan be used to select a range of variables. Seelanguagefor more details.- times
The number of permutation samples.
- apparent
A logical. Should an extra resample be added where the analysis is the standard data set.
- ...
Not currently used.
Value
A tibble with classes permutations, rset, tbl_df, tbl, and
data.frame. The results include a column for the data split objects and a
column called id that has a character string with the resample
identifier.
Details
The argument apparent enables the option of an additional
"resample" where the analysis data set is the same as the original data
set. Permutation-based resampling can be especially helpful for computing
a statistic under the null hypothesis (e.g. t-statistic). This forms the
basis of a permutation test, which computes a test statistic under all
possible permutations of the data.
Examples
permutations(mtcars, mpg, times = 2)
#> # Permutation sampling
#> # Permuted columns: [mpg]
#> # A tibble: 2 × 2
#> splits id
#> <list> <chr>
#> 1 <split [32/0]> Permutations1
#> 2 <split [32/0]> Permutations2
permutations(mtcars, mpg, times = 2, apparent = TRUE)
#> # Permutation sampling with apparent sample
#> # Permuted columns: [mpg]
#> # A tibble: 3 × 2
#> splits id
#> <list> <chr>
#> 1 <split [32/0]> Permutations1
#> 2 <split [32/0]> Permutations2
#> 3 <split [32/32]> Apparent
library(purrr)
resample1 <- permutations(mtcars, starts_with("c"), times = 1)
resample1$splits[[1]] %>% analysis()
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 1
#> Mazda RX4 Wag 21.0 4 160.0 110 3.90 2.875 17.02 0 1 4 2
#> Datsun 710 22.8 8 108.0 93 3.85 2.320 18.61 1 1 4 4
#> Hornet 4 Drive 21.4 8 258.0 110 3.08 3.215 19.44 1 0 3 4
#> Hornet Sportabout 18.7 4 360.0 175 3.15 3.440 17.02 0 0 3 1
#> Valiant 18.1 8 225.0 105 2.76 3.460 20.22 1 0 3 4
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 2
#> Merc 240D 24.4 8 146.7 62 3.69 3.190 20.00 1 0 4 3
#> Merc 230 22.8 8 140.8 95 3.92 3.150 22.90 1 0 4 4
#> Merc 280 19.2 8 167.6 123 3.92 3.440 18.30 1 0 4 2
#> Merc 280C 17.8 8 167.6 123 3.92 3.440 18.90 1 0 4 2
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 4
#> Merc 450SL 17.3 6 275.8 180 3.07 3.730 17.60 0 0 3 4
#> Merc 450SLC 15.2 6 275.8 180 3.07 3.780 18.00 0 0 3 4
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 8
#> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 2
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 3
#> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 2
#> Honda Civic 30.4 6 75.7 52 4.93 1.615 18.52 1 1 4 1
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 2
#> Toyota Corona 21.5 6 120.1 97 3.70 2.465 20.01 1 0 3 4
#> Dodge Challenger 15.5 4 318.0 150 2.76 3.520 16.87 0 0 3 1
#> AMC Javelin 15.2 4 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Camaro Z28 13.3 4 350.0 245 3.73 3.840 15.41 0 0 3 2
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 3
#> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
#> Porsche 914-2 26.0 6 120.3 91 4.43 2.140 16.70 0 1 5 4
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 1
#> Ford Pantera L 15.8 4 351.0 264 4.22 3.170 14.50 0 1 5 1
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 4
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
resample2 <- permutations(mtcars, hp, times = 10, apparent = TRUE)
map_dbl(resample2$splits, function(x) {
t.test(hp ~ vs, data = analysis(x))$statistic
})
#> [1] 1.831884490 0.360219662 -1.271345514 -1.086517310 0.884050160
#> [6] 1.130681222 0.369342268 -2.595445455 0.007920257 0.562836352
#> [11] 6.290837794