stapler (version 0.8.0)

staple_multi_mat: STAPLE on Multi-class matrix

Description

STAPLE on Multi-class matrix

Usage

staple_multi_mat(
  x,
  sens_init = 0.99999,
  spec_init = 0.99999,
  max_iter = 10000,
  tol = .Machine$double.eps,
  prior = "mean",
  verbose = TRUE,
  trace = 25,
  ties.method = c("first", "random", "last"),
  drop_all_same = FALSE
)

Value

List of matrix output sensitivities, specificities, and matrix of probabilities

Arguments

x

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

sens_init

Initialize matrix for sensitivity (p)

spec_init

Initialize matrix for specificity (q)

max_iter

Maximum number of iterations to run

tol

Tolerance for convergence

prior

Either "mean" or a matrix of prior probabilities,

verbose

print diagnostic messages

trace

Number for modulus to print out verbose iterations

ties.method

Method passed to max.col for hard segmentation

drop_all_same

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

Examples

rm(list = ls())
x = matrix(rbinom(5000, size = 5, prob = 0.5), ncol = 1000)
  sens_init = 0.99999
  spec_init = 0.99999
  max_iter = 10000
  tol = .Machine$double.eps
  prior = "mean"
  verbose = TRUE
  trace = 25
  ties.method = "first"

res = staple_multi_mat(x)
xx = rbind(colMeans(x >= 2) > 0.5, colMeans(x >= 2) >= 0.5)
res = staple_multi_mat(xx, prior = rep(0.5, 1000))
res_bin = staple_bin_mat(xx, prior = rep(0.5, 1000))
testthat::expect_equal(res$sensitivity[,"1"], res_bin$sensitivity)