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,] 1.60877501 -1.0915715 0.9864177 1.15119402 0.50365091 -0.1031018
#> [2,] 0.84824048 0.5036509 1.1252749 -0.09823232 1.60877501 -1.3538170
#> [3,] -0.02931659 1.1511940 1.1252749 -0.65159647 -0.09823232 1.6087750
#> [4,] -0.65159647 1.1252749 -1.3538170 -1.09157153 2.03635806 0.2859314
#> [5,] -0.09823232 0.2859314 -1.3538170 -0.65159647 -0.10310177 0.6504066
#> [6,] -1.35381698 -0.3645642 -0.3180002 0.50365091 1.12527487 -0.1031018
#> [7,] -0.09823232 0.5717264 -0.1031018 -0.36456419 0.50365091 0.8482405
#> [8,] 0.65040660 2.0363581 -1.3538170 -0.36456419 1.12527487 -0.2043277
#> [9,] -0.09823232 -0.3645642 0.5717264 -0.20432766 1.60877501 1.1252749
#> [10,] 2.03635806 -1.0915715 -1.3538170 -0.36456419 0.84824048 0.2859314
#> [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] -0.02931659 2.0363581 1.12527487 -0.20432766 0.8482405 -0.09823232
#> [2,] 0.65040660 -0.3180002 -0.10310177 -0.65159647 0.2859314 -0.02931659
#> [3,] 0.98641772 0.8482405 0.65040660 -1.35381698 -0.8807304 0.28593139
#> [4,] 0.65040660 -0.2043277 0.98641772 -0.10310177 -0.8807304 -0.36456419
#> [5,] 0.84824048 -1.0915715 -0.02931659 1.12527487 0.9864177 0.57172643
#> [6,] 0.98641772 0.8482405 0.65040660 -0.88073038 1.6087750 -0.65159647
#> [7,] 0.28593139 -0.6515965 0.98641772 -0.88073038 -1.3538170 -0.02931659
#> [8,] 1.60877501 -1.0915715 -0.65159647 -0.10310177 0.5036509 0.98641772
#> [9,] -0.10310177 -0.3180002 0.28593139 -0.65159647 2.0363581 -0.88073038
#> [10,] 0.65040660 1.1252749 -0.65159647 -0.02931659 0.9864177 0.57172643
#> [,13] [,14] [,15] [,16] [,17] [,18]
#> [1,] -0.31800023 0.57172643 0.6504066 -1.35381698 -0.88073038 -0.65159647
#> [2,] -0.36456419 2.03635806 -1.0915715 0.57172643 0.98641772 1.15119402
#> [3,] -0.10310177 -1.09157153 -0.2043277 0.57172643 -0.31800023 0.50365091
#> [4,] -0.09823232 1.60877501 0.8482405 0.57172643 -0.02931659 0.50365091
#> [5,] -0.31800023 1.15119402 -0.2043277 -0.36456419 2.03635806 1.60877501
#> [6,] -0.09823232 -0.02931659 -1.0915715 2.03635806 -0.20432766 0.28593139
#> [7,] -0.31800023 1.15119402 -1.0915715 2.03635806 1.12527487 0.65040660
#> [8,] 0.57172643 0.84824048 1.1511940 -0.02931659 -0.88073038 -0.09823232
#> [9,] 0.84824048 1.15119402 0.6504066 -1.35381698 -1.09157153 0.50365091
#> [10,] -0.10310177 -0.20432766 1.1511940 -0.09823232 -0.88073038 0.50365091
#> [,19] [,20]
#> [1,] 0.28593139 -0.3645642
#> [2,] -0.20432766 -0.8807304
#> [3,] 2.03635806 -0.3645642
#> [4,] -0.31800023 1.1511940
#> [5,] 0.50365091 -0.8807304
#> [6,] 1.15119402 0.5717264
#> [7,] 1.60877501 -0.2043277
#> [8,] -0.31800023 0.2859314
#> [9,] -0.02931659 0.9864177
#> [10,] 1.60877501 -0.3180002
## 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,] 60 54 38 106 62 41 65 38 63 37 95 11 63
#> [2,] 67 68 36 102 67 47 77 44 64 35 76 16 50
#> [3,] 67 57 43 104 63 46 63 31 58 28 92 17 69
#> [4,] 66 60 35 103 67 45 82 43 56 23 87 13 65
#> [5,] 75 68 42 90 43 43 69 38 70 30 86 10 56
#> [6,] 69 53 41 91 58 51 54 33 64 33 88 15 74
#> [7,] 73 70 32 72 65 42 77 40 72 35 82 12 58
#> [8,] 83 70 38 105 60 41 69 41 60 23 93 15 56
#> [9,] 68 71 37 101 65 31 68 27 56 32 108 13 63
#> [10,] 59 54 48 101 57 46 91 38 56 32 94 16 67
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,] 12 36 67 9 86 1 23
#> [2,] 20 32 76 8 65 1 16
#> [3,] 11 45 63 12 68 1 29
#> [4,] 14 21 72 12 79 1 23
#> [5,] 5 54 68 13 78 0 29
#> [6,] 12 42 82 10 77 1 19
#> [7,] 6 44 82 11 64 2 28
#> [8,] 6 35 72 7 66 4 23
#> [9,] 7 33 74 9 68 0 36
#> [10,] 8 27 72 11 67 1 22