Often you will resample a dataset hundreds or thousands of times. Storing
the complete resample each time would be very inefficient so this class
instead stores a "pointer" to the original dataset, and a vector of row
indexes. To turn this into a regular data frame, call as.data.frame,
to extract the indices, use as.integer.
Arguments
- data
The data frame
- idx
A vector of integer indexes indicating which rows have been selected. These values should lie between 1 and
nrow(data)but they are not checked by this function in the interests of performance.
See also
Other resampling techniques:
bootstrap(),
resample_bootstrap(),
resample_partition()
Examples
resample(mtcars, 1:10)
#> <resample [10 x 11]> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
b <- resample_bootstrap(mtcars)
b
#> <resample [32 x 11]> 23, 29, 23, 13, 5, 11, 6, 10, 5, 14, ...
as.integer(b)
#> [1] 23 29 23 13 5 11 6 10 5 14 24 28 19 30 20 24 31 32 24 21 1 15 24
#> [24] 28 7 4 12 11 4 2 13 25
as.data.frame(b)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> AMC Javelin.1 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
#> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
#> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
#> Hornet Sportabout.1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
#> Camaro Z28.1 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> 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
#> Camaro Z28.2 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
#> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Camaro Z28.3 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Lotus Europa.1 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Merc 280C.1 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
#> Hornet 4 Drive.1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
#> Merc 450SL.1 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
# Many modelling functions will do the coercion for you, so you can
# use a resample object directly in the data argument
lm(mpg ~ wt, data = b)
#>
#> Call:
#> lm(formula = mpg ~ wt, data = b)
#>
#> Coefficients:
#> (Intercept) wt
#> 39.46 -6.30
#>