Draws a random (sub)sample from a multinomial distribution.
Usage
resample_multinomial(object, ...)
# S4 method for class 'numeric'
resample_multinomial(object, n, size = sum(object), ...)Value
A numeric matrix with n rows and k columns.
See also
Other resampling methods:
bootstrap(),
jackknife(),
resample_uniform()
Examples
## Uniform distribution
x <- rnorm(20)
resample_uniform(x, n = 10)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 2.34400339 -0.66750902 0.5277432 0.1169515 -0.31385587 0.4791398
#> [2,] 0.11695150 0.52774319 -0.5715427 0.1296863 0.01879218 -0.6863439
#> [3,] -0.31385587 -0.68634388 -0.5715427 0.1169515 1.38700704 0.8474670
#> [4,] -0.57154269 -0.18183487 -0.3008754 0.3100714 0.52774319 1.3870070
#> [5,] -0.31385587 1.24879410 1.3870070 -0.1818349 0.31007138 0.2039750
#> [6,] 0.84746696 0.01879218 -0.5715427 -0.1818349 0.52774319 0.3452646
#> [7,] -0.31385587 -0.18183487 1.2487941 0.3452646 0.47913980 0.5277432
#> [8,] 0.01879218 0.12968631 -0.5715427 -0.1818349 0.20397498 -0.6863439
#> [9,] 0.84746696 2.34400339 -1.2917085 0.3100714 -0.30087541 0.3452646
#> [10,] -0.30087541 0.20397498 -0.1818349 0.4791398 0.12968631 0.3452646
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] 0.5469201 0.20397498 0.84746696 -0.5715427 -1.29170853 -0.6863439
#> [2,] 0.8474670 0.34526460 0.54692012 1.3870070 -1.29170853 -0.1818349
#> [3,] 0.2039750 0.12968631 2.34400339 0.5277432 0.54692012 1.2487941
#> [4,] 0.5469201 0.20397498 0.84746696 -1.2917085 0.47913980 0.1169515
#> [5,] -0.6863439 0.11695150 0.54692012 -1.2917085 -0.57154269 2.3440034
#> [6,] 0.4791398 0.12968631 0.11695150 1.3870070 0.31007138 0.5469201
#> [7,] 1.3870070 -0.30087541 -0.68634388 0.1169515 0.01879218 -1.2917085
#> [8,] 0.8474670 0.52774319 0.11695150 2.3440034 0.54692012 1.2487941
#> [9,] -0.6675090 0.54692012 0.01879218 -0.3138559 -0.18183487 0.5277432
#> [10,] -0.6675090 0.01879218 -0.57154269 0.8474670 -0.31385587 1.2487941
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] 1.3870070 0.1296863 0.3452646 1.24879410 -0.30087541 0.3100714
#> [2,] -0.3138559 0.4791398 0.2039750 1.24879410 2.34400339 0.3100714
#> [3,] -0.3008754 -0.6675090 0.3452646 -0.18183487 0.01879218 0.4791398
#> [4,] -0.3138559 2.3440034 0.1296863 0.01879218 0.34526460 -0.6863439
#> [5,] -0.3008754 -0.6675090 0.1296863 0.01879218 0.52774319 0.8474670
#> [6,] 1.2487941 0.2039750 -0.6675090 2.34400339 -1.29170853 -0.3138559
#> [7,] 0.2039750 -0.6675090 0.8474670 -0.57154269 0.12968631 0.3100714
#> [8,] 1.3870070 0.3452646 -0.6675090 -0.31385587 -1.29170853 0.3100714
#> [9,] 1.2487941 0.4791398 -0.5715427 0.11695150 0.20397498 0.1296863
#> [10,] -1.2917085 0.1169515 0.3100714 0.54692012 1.38700704 0.5277432
#> [,19] [,20]
#> [1,] 0.01879218 -0.1818349
#> [2,] -0.30087541 -0.6675090
#> [3,] 0.31007138 -1.2917085
#> [4,] -0.66750902 1.2487941
#> [5,] 0.47913980 0.3452646
#> [6,] -0.30087541 -0.6863439
#> [7,] 2.34400339 0.5469201
#> [8,] 0.47913980 -0.3008754
#> [9,] -0.68634388 1.3870070
#> [10,] 2.34400339 -0.6863439
## Multinomial distribution
x <- sample(1:100, 20, TRUE)
resample_multinomial(x, n = 10)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 58 6 8 50 61 70 39 94 42 33 88 70 2
#> [2,] 60 3 9 46 80 71 37 77 28 25 78 83 1
#> [3,] 60 4 10 57 77 62 41 82 25 22 90 81 2
#> [4,] 51 6 11 57 70 46 33 83 36 19 79 81 0
#> [5,] 57 3 6 44 79 67 36 93 40 33 81 73 0
#> [6,] 65 5 7 58 68 49 40 89 21 26 74 88 1
#> [7,] 68 4 10 45 72 64 32 92 32 29 86 83 0
#> [8,] 76 5 7 50 67 54 26 78 38 29 96 84 1
#> [9,] 62 8 13 55 66 65 43 85 25 26 82 82 0
#> [10,] 68 5 8 55 60 75 46 83 40 26 80 78 1
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,] 54 88 14 42 90 39 53
#> [2,] 60 92 26 52 94 32 47
#> [3,] 56 94 12 49 87 30 60
#> [4,] 64 79 20 48 96 50 72
#> [5,] 60 86 12 46 81 37 67
#> [6,] 78 94 14 47 76 43 58
#> [7,] 59 79 22 44 95 30 55
#> [8,] 68 83 19 46 76 39 59
#> [9,] 66 83 22 50 81 36 51
#> [10,] 71 78 10 40 93 27 57