Calculate WoE for sample-specific error probabilities integrated over the donor prior distribution using numerical integration

Calculate WoE for sample-specific error probabilities integrated over the donor prior distribution using numerical integration

Usage

calc_WoE_wTwR_integrate_wT_num(xT, xR, shape1T, shape2T, wR, p)

Arguments

xT

profile from case (of 0, 1, 2)

xR

profile from suspect (of 0, 1, 2)

shape1T

wT has beta prior on (0, 0.5) with parameters shape1T and shape2T

shape2T

see shape1T_Hp

wR

error probability for PoI sample

p

list of genotype probabilities (same length as xT/xR, or vector of length 3 for reuse)

Examples

calc_LRs_wTwR(xT = c(0, 0), xR = c(0, 1), wT = 1e-2, wR = 1e-5, p = c(0.25, 0.25, 0.5)) |> log10() |> sum()
#> [1] -0.7995914

shpT <- get_beta_parameters(mu = 1e-2, sigmasq = 1e-7, a = 0, b = 0.5)
# curve(dbeta05(x, shpT[1], shpT[2]), from = 0, to = 0.1, n = 1001)
z1 <- calc_WoE_wTwR_integrate_wT_mc(
  xT = c(0, 0), 
  xR = c(0, 1), 
  shape1T = shpT[1], shape2T = shpT[2],
  wR = 1e-5, 
  p = c(0.25, 0.25, 0.5),
  n_samples = 1000)
z1$WoE
#> [1] -0.7989807
z1$WoEs; sum(z1$WoEs)
#> [1]  0.5975943 -1.3965750
#> [1] -0.7989807

z2 <- calc_WoE_wTwR_integrate_wT_num(
  xT = c(0, 0), 
  xR = c(0, 1), 
  shape1T = shpT[1], shape2T = shpT[2], 
  wR = 1e-5, 
  p = c(0.25, 0.25, 0.5))
z2$WoE
#> [1] -0.7998079
z2$WoEs; sum(z2$WoEs)
#> [1]  0.597603 -1.397411
#> [1] -0.7998079