Same latent genotype, Z, with independent errors for true donor (D) and 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_Hp_w(n = 10, w = 0.3, p = c(0.25, 0.25, 0.5))
#> $X_D
#> [,1]
#> [1,] 1
#> [2,] 2
#> [3,] 1
#> [4,] 1
#> [5,] 2
#> [6,] 1
#> [7,] 2
#> [8,] 2
#> [9,] 1
#> [10,] 1
#>
#> $X_S
#> [,1]
#> [1,] 1
#> [2,] 1
#> [3,] 2
#> [4,] 1
#> [5,] 1
#> [6,] 1
#> [7,] 0
#> [8,] 1
#> [9,] 1
#> [10,] 0
#>
sample_data_Hp_w(n = 10, w = 0.1, p = list(
c(0.25, 0.25, 0.5), c(0.1, 0.8, 0.1)))
#> $X_D
#> [,1] [,2]
#> [1,] 2 1
#> [2,] 2 1
#> [3,] 1 0
#> [4,] 0 1
#> [5,] 0 1
#> [6,] 2 2
#> [7,] 1 1
#> [8,] 1 1
#> [9,] 2 1
#> [10,] 2 2
#>
#> $X_S
#> [,1] [,2]
#> [1,] 2 1
#> [2,] 2 1
#> [3,] 0 1
#> [4,] 1 1
#> [5,] 0 1
#> [6,] 2 1
#> [7,] 1 1
#> [8,] 0 1
#> [9,] 2 1
#> [10,] 0 2
#>
cases <- sample_data_Hp_w(n = 1000, w = 0.3, p = c(0.25, 0.25, 0.5))
tab <- table(X_D = cases$X_D, X_S = cases$X_S)
tab
#> X_S
#> X_D 0 1 2
#> 0 65 92 50
#> 1 84 213 141
#> 2 56 158 141
estimate_w(tab)
#> [1] 0.3358798
cases <- sample_data_Hp_w(n = 1000, w = 0, p = c(0.1, 0.7, 0.2))
tab <- table(X_D = cases$X_D, X_S = cases$X_S)
diag(tab/sum(tab))
#> 0 1 2
#> 0.111 0.702 0.187