A "lazy" resample.

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.

resample(data, idx)

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.

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]> 20, 18, 17, 12, 25, 32, 20, 6, 25, 19, ...
as.integer(b)
#>  [1] 20 18 17 12 25 32 20  6 25 19  4 30 29 12 28  7 22  2 31  9 20  9 29 29 32
#> [26] 12 27 19 25 25 16 25
as.data.frame(b)
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
#> Toyota Corolla.1    33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Pontiac Firebird.1  19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Merc 450SE.1        16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Lotus Europa        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
#> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Toyota Corolla.2    33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Merc 230.1          22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Ford Pantera L.1    15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Ford Pantera L.2    15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Volvo 142E.1        21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
#> Merc 450SE.2        16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Honda Civic.1       30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Pontiac Firebird.2  19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Pontiac Firebird.3  19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Pontiac Firebird.4  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  
#>      39.585       -5.777  
#>

Arguments

See also

Examples