One latent genotype, ZD, for true donor (D) and one latent genotype, ZS, for suspect (S).
Arguments
- n
number of samples
- w
error probability
- p
list of genotype probabilities (length is number of loci) or vector of length 3 for single locus
- ...
Passed on to
add_errors_to_genotypes()
Value
list of two matrices, each of size n x loci with genotype in 0/1/2 format resembling the situation in real life.
Examples
sample_data_Hd_w(n = 10, w = 0.3, p = c(0.25, 0.25, 0.5))
#> $xT
#> [,1]
#> [1,] 1
#> [2,] 1
#> [3,] 1
#> [4,] 0
#> [5,] 2
#> [6,] 1
#> [7,] 0
#> [8,] 1
#> [9,] 0
#> [10,] 1
#>
#> $xR
#> [,1]
#> [1,] 1
#> [2,] 2
#> [3,] 2
#> [4,] 1
#> [5,] 2
#> [6,] 0
#> [7,] 2
#> [8,] 2
#> [9,] 1
#> [10,] 2
#>
sample_data_Hd_w(n = 10, w = 0.1, p = list(
c(0.25, 0.25, 0.5), c(0.1, 0.8, 0.1)))
#> $xT
#> [,1] [,2]
#> [1,] 1 2
#> [2,] 0 0
#> [3,] 2 1
#> [4,] 2 1
#> [5,] 0 1
#> [6,] 1 0
#> [7,] 0 1
#> [8,] 0 1
#> [9,] 2 1
#> [10,] 2 1
#>
#> $xR
#> [,1] [,2]
#> [1,] 0 1
#> [2,] 2 2
#> [3,] 1 1
#> [4,] 1 1
#> [5,] 0 1
#> [6,] 2 1
#> [7,] 0 1
#> [8,] 2 1
#> [9,] 1 2
#> [10,] 0 1
#>
cases <- sample_data_Hd_w(n = 1000, w = 0, p = c(0.25, 0.25, 0.5))
tab <- table(xT = cases$xT, xR = cases$xR)
tab
#> xR
#> xT 0 1 2
#> 0 61 59 122
#> 1 72 54 118
#> 2 129 128 257