Calculate WoE for a profile for sample-specific error probabilities integrated over the donor prior distribution using Monte Carlo integration

Usage

calc_WoE_wTwR_integrate_wT_mc(
  xT,
  xR,
  shape1T_Hp,
  shape2T_Hp,
  shape1T_Ha,
  shape2T_Ha,
  wR,
  p,
  n_samples = 1000
)

Arguments

xT: profile from case (of 0, 1, 2)
xR: profile from suspect (of 0, 1, 2)
shape1T_Hp: under $H_p$ wT has beta prior on (0, 0.5) with parameters shape1T_Hp and shape2T_Hp
shape2T_Hp: see shape1T_Hp
shape1T_Ha: under $H_a$ wT has beta prior on (0, 0.5) with parameters shape1T_Ha and shape2T_Ha
shape2T_Ha: see shape1T_Ha
wR: error probability for PoI sample
p: list of genotype probabilities (same length as xT/xR, or vector of length 3 for reuse)
n_samples: number of random samples from each prior distribution

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_Hp = shpT[1], 
  shape2T_Hp = shpT[2],
  shape1T_Ha = shpT[1], 
  shape2T_Ha = shpT[2],
  wR = 1e-5, 
  p = c(0.25, 0.25, 0.5),
  n_samples = 1000)
z1
#> [1] -0.7992314

z2 <- calc_WoE_wTwR_integrate_wT_num(
  xT = c(0, 0), 
  xR = c(0, 1), 
  shape1T_Hp = shpT[1], 
  shape2T_Hp = shpT[2],
  shape1T_Ha = shpT[1], 
  shape2T_Ha = shpT[2],
  wR = 1e-5, 
  p = c(0.25, 0.25, 0.5))
z2
#> [1] -0.7996046