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]> 32, 5, 8, 12, 5, 4, 4, 29, 4, 17, ...
as.integer(b)
#> [1] 32 5 8 12 5 4 4 29 4 17 30 15 14 27 9 24 22 14 12 13 3 28 15
#> [24] 31 13 13 17 22 29 31 8 21
as.data.frame(b)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
#> Hornet Sportabout.1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
#> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Hornet 4 Drive.1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Hornet 4 Drive.2 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
#> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
#> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
#> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
#> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
#> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> Merc 450SLC.1 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
#> Merc 450SE.1 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
#> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
#> Cadillac Fleetwood.1 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
#> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Merc 450SL.1 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Merc 450SL.2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
#> Chrysler Imperial.1 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
#> Dodge Challenger.1 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
#> Ford Pantera L.1 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
#> Maserati Bora.1 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#> Merc 240D.1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
#> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
# 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
#> 32.984 -4.172
#>