Permutation sampling

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.

Usage

permutations(data, permute = NULL, times = 25, apparent = FALSE, ...)

Arguments

data

A data frame.

permute

One or more columns to shuffle. This argument supports tidyselect selectors. Multiple expressions can be combined with c(). Variable names can be used as if they were positions in the data frame, so expressions like x:y can be used to select a range of variables. See language for 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.

...

These dots are for future extensions and must be empty.

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