staple_bin_mat {stapler}

STAPLE on binary matrix

Description

STAPLE on binary matrix

Usage

staple_bin_mat(
  x,
  sens_init = 0.99999,
  spec_init = 0.99999,
  max_iter = 10000,
  tol = .Machine$double.eps,
  prior = "mean",
  verbose = TRUE,
  trace = 10,
  drop_all_same = FALSE
)

Arguments

x

a nxr matrix where there are n raters and r elements rated

sens_init

Initialize parameter for sensitivity (p)

spec_init

Initialize parameter for specificity (q)

max_iter

Maximum number of iterations to run

tol

Tolerance for convergence

prior

Either "mean" or a vector of prior probabilities,

verbose

print diagnostic messages

trace

Number for modulus to print out verbose iterations

drop_all_same

drop all records where they are all the same. DO NOT use in practice, only for validation of past results

Value

List of output sensitivities, specificities, and vector of probabilities

Examples

n = 5
r = 1000
sens = c(0.8, 0.9, 0.8, 0.5, 0.8)
spec = c(0.9, 0.75, 0.99, 0.98, 0.92)
suppressWarnings(RNGversion("3.5.0"))
set.seed(20171120)
n_1 = 200
n_0 = r - n_1
truth = c(rep(0, n_0), rep(1, n_1))
pred_1 = rbinom(n = n, size = n_1, prob = sens)
pred_0 = rbinom(n = n, size = n_0, prob = spec)
pred_0 = sapply(pred_0, function(n) {
   sample(c(rep(0, n), rep(1, n_0 -n)))
})
pred_1 = sapply(pred_1, function(n) {
   sample(c(rep(1, n), rep(0, n_1 -n)))
})
pred = rbind(pred_0, pred_1)
true_sens = colMeans(pred[ truth == 1, ])
true_spec = colMeans(1-pred[ truth == 0, ])
x = t(pred)
staple_out = staple_bin_mat(x)
testthat::expect_equal(staple_out$sensitivity,
c(0.781593858553476, 0.895868301462594,
0.760514086161722, 0.464483444340873,
0.765239314719065))
staple_out_prior = staple_bin_mat(x, prior = rep(0.5, r))
testthat::expect_equal(staple_out_prior$sensitivity,
c(0.683572080864211, 0.821556768891859,
0.619166852992802, 0.389409921992467, 0.67042085955546))