staple_multi_mat: STAPLE on Multi-class matrix

In neuroconductor/stapler: Simultaneous Truth and Performance Level Estimation

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
)

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

Value

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

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)

neuroconductor/stapler documentation built on May 20, 2021, 6:39 p.m.