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.
permutations(data, permute = NULL, times = 25, apparent = FALSE, ...)
data | A data frame. |
---|---|
permute | One or more columns to shuffle. This argument supports
|
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. |
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.
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.
permutations(mtcars, mpg, times = 2)#> # Permutation sampling #> # Permuted columns: [mpg] #> # A tibble: 2 x 2 #> splits id #> <list> <chr> #> 1 <split [32/0]> Permutations1 #> 2 <split [32/0]> Permutations2permutations(mtcars, mpg, times = 2, apparent = TRUE)#> # Permutation sampling with apparent sample #> # Permuted columns: [mpg] #> # A tibble: 3 x 2 #> splits id #> <list> <chr> #> 1 <split [32/0]> Permutations1 #> 2 <split [32/0]> Permutations2 #> 3 <split [32/32]> Apparentlibrary(purrr) resample1 <- permutations(mtcars, starts_with("c"), times = 1) resample1$splits[[1]] %>% analysis()#> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 1 #> Mazda RX4 Wag 21.0 4 160.0 110 3.90 2.875 17.02 0 1 4 2 #> Datsun 710 22.8 8 108.0 93 3.85 2.320 18.61 1 1 4 4 #> Hornet 4 Drive 21.4 8 258.0 110 3.08 3.215 19.44 1 0 3 4 #> Hornet Sportabout 18.7 4 360.0 175 3.15 3.440 17.02 0 0 3 1 #> Valiant 18.1 8 225.0 105 2.76 3.460 20.22 1 0 3 4 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 2 #> Merc 240D 24.4 8 146.7 62 3.69 3.190 20.00 1 0 4 3 #> Merc 230 22.8 8 140.8 95 3.92 3.150 22.90 1 0 4 4 #> Merc 280 19.2 8 167.6 123 3.92 3.440 18.30 1 0 4 2 #> Merc 280C 17.8 8 167.6 123 3.92 3.440 18.90 1 0 4 2 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 4 #> Merc 450SL 17.3 6 275.8 180 3.07 3.730 17.60 0 0 3 4 #> Merc 450SLC 15.2 6 275.8 180 3.07 3.780 18.00 0 0 3 4 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 8 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 2 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 3 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 2 #> Honda Civic 30.4 6 75.7 52 4.93 1.615 18.52 1 1 4 1 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 2 #> Toyota Corona 21.5 6 120.1 97 3.70 2.465 20.01 1 0 3 4 #> Dodge Challenger 15.5 4 318.0 150 2.76 3.520 16.87 0 0 3 1 #> AMC Javelin 15.2 4 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 4 350.0 245 3.73 3.840 15.41 0 0 3 2 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 3 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 6 120.3 91 4.43 2.140 16.70 0 1 5 4 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 1 #> Ford Pantera L 15.8 4 351.0 264 4.22 3.170 14.50 0 1 5 1 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 4 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2resample2 <- permutations(mtcars, hp, times = 10, apparent = TRUE) map_dbl(resample2$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