Simulate data for minimisation simulations
simulate_data.RdGiven a list of factors with specified levels and proportions, and a sample
size, this function simulates data for use in simulate_mini().
Arguments
- sampsize
the desired sample size of the data, i.e., the sample size of your prospective trial.
- factors
a list of factors, each a list containing two items. The first is
levelswhich is either a vector of the level names OR the number of levels. The second item is eitherprops, a vector of proportions equal to the number of levels in the factor; OR prop.dist which is a named vector containing the mean and sd of the proportions.
Examples
simulate_data(factors = list(sex = list(levels = c("M", "F"),
props = c(0.5, 0.5)),
site = list(levels = 12,
prop.dist = c(mean = 0.1, sd = 0.05))),
sampsize = 150)
#> ID sex site
#> 1 1 M 12
#> 2 2 M 1
#> 3 3 M 2
#> 4 4 F 5
#> 5 5 F 10
#> 6 6 F 10
#> 7 7 M 3
#> 8 8 F 12
#> 9 9 F 4
#> 10 10 F 3
#> 11 11 M 5
#> 12 12 F 12
#> 13 13 F 4
#> 14 14 M 3
#> 15 15 M 9
#> 16 16 F 4
#> 17 17 F 12
#> 18 18 M 6
#> 19 19 M 11
#> 20 20 F 3
#> 21 21 F 3
#> 22 22 M 5
#> 23 23 F 1
#> 24 24 M 6
#> 25 25 M 9
#> 26 26 F 3
#> 27 27 M 4
#> 28 28 M 6
#> 29 29 F 5
#> 30 30 F 8
#> 31 31 F 8
#> 32 32 M 8
#> 33 33 M 3
#> 34 34 F 5
#> 35 35 M 2
#> 36 36 F 4
#> 37 37 M 10
#> 38 38 F 9
#> 39 39 F 4
#> 40 40 M 10
#> 41 41 M 2
#> 42 42 M 5
#> 43 43 M 3
#> 44 44 M 12
#> 45 45 F 3
#> 46 46 M 12
#> 47 47 M 12
#> 48 48 F 6
#> 49 49 M 8
#> 50 50 F 7
#> 51 51 F 10
#> 52 52 F 12
#> 53 53 F 11
#> 54 54 M 10
#> 55 55 F 12
#> 56 56 M 6
#> 57 57 F 3
#> 58 58 F 10
#> 59 59 M 3
#> 60 60 M 5
#> 61 61 F 7
#> 62 62 F 1
#> 63 63 M 12
#> 64 64 F 12
#> 65 65 F 10
#> 66 66 F 4
#> 67 67 F 12
#> 68 68 M 2
#> 69 69 M 5
#> 70 70 F 3
#> 71 71 M 6
#> 72 72 M 1
#> 73 73 F 12
#> 74 74 F 1
#> 75 75 F 1
#> 76 76 F 3
#> 77 77 F 7
#> 78 78 F 6
#> 79 79 F 9
#> 80 80 F 12
#> 81 81 F 9
#> 82 82 M 10
#> 83 83 M 10
#> 84 84 M 12
#> 85 85 F 7
#> 86 86 F 6
#> 87 87 F 6
#> 88 88 M 8
#> 89 89 M 6
#> 90 90 M 8
#> 91 91 M 10
#> 92 92 F 6
#> 93 93 F 8
#> 94 94 M 3
#> 95 95 F 5
#> 96 96 M 10
#> 97 97 M 7
#> 98 98 F 3
#> 99 99 M 12
#> 100 100 F 12
#> 101 101 M 8
#> 102 102 M 3
#> 103 103 M 3
#> 104 104 M 5
#> 105 105 M 6
#> 106 106 M 3
#> 107 107 M 2
#> 108 108 M 3
#> 109 109 F 10
#> 110 110 F 7
#> 111 111 M 4
#> 112 112 F 5
#> 113 113 F 6
#> 114 114 M 4
#> 115 115 M 3
#> 116 116 F 12
#> 117 117 M 3
#> 118 118 M 3
#> 119 119 M 10
#> 120 120 M 9
#> 121 121 M 2
#> 122 122 F 6
#> 123 123 F 1
#> 124 124 F 3
#> 125 125 M 10
#> 126 126 M 4
#> 127 127 M 12
#> 128 128 M 1
#> 129 129 F 2
#> 130 130 F 3
#> 131 131 F 4
#> 132 132 M 8
#> 133 133 F 10
#> 134 134 M 4
#> 135 135 F 12
#> 136 136 F 3
#> 137 137 M 12
#> 138 138 M 3
#> 139 139 M 4
#> 140 140 M 3
#> 141 141 F 12
#> 142 142 M 3
#> 143 143 F 12
#> 144 144 F 3
#> 145 145 M 8
#> 146 146 M 6
#> 147 147 M 6
#> 148 148 F 10
#> 149 149 M 4
#> 150 150 F 7