STAPLE on Multi-class matrix
staple_multi_mat(
x,
sens_init = 0.99999,
spec_init = 0.99999,
max_iter = 10000,
tol = .Machine$double.eps,
prior = "mean",
verbose = TRUE,
trace = 25,
ties.method = c("first", "random", "last"),
drop_all_same = FALSE
)| x | a nxr matrix where there are n raters and r elements rated |
|---|---|
| sens_init | Initialize matrix for sensitivity (p) |
| spec_init | Initialize matrix for specificity (q) |
| max_iter | Maximum number of iterations to run |
| tol | Tolerance for convergence |
| prior | Either "mean" or a matrix of prior probabilities, |
| verbose | print diagnostic messages |
| trace | Number for modulus to print out verbose iterations |
| ties.method | Method passed to |
| drop_all_same | drop all records where they are all the same. DO NOT use in practice, only for validation of past results |
List of matrix output sensitivities, specificities, and matrix of probabilities
rm(list = ls())
x = matrix(rbinom(5000, size = 5, prob = 0.5), ncol = 1000)
sens_init = 0.99999
spec_init = 0.99999
max_iter = 10000
tol = .Machine$double.eps
prior = "mean"
verbose = TRUE
trace = 25
ties.method = "first"
res = staple_multi_mat(x)
#> There are 6 levels present
#> Removing elements where all raters agree
#> Making multiple, matrices. Hot-one encode
#> iter: 25, diff: 5.69431779326446e-06
#> iter: 50, diff: 1.11233938726585e-09
#> iter: 75, diff: 2.41306974402278e-13
#> Convergence!
xx = rbind(colMeans(x >= 2) > 0.5, colMeans(x >= 2) >= 0.5)
res = staple_multi_mat(xx, prior = rep(0.5, 1000))
#> There are 2 levels present
#> Removing elements where all raters agree
#> Making multiple, matrices. Hot-one encode
#> Convergence!
res_bin = staple_bin_mat(xx, prior = rep(0.5, 1000))
#> iter: 1, diff: 9.99998203143893e-06
#> Convergence!
testthat::expect_equal(res$sensitivity[,"1"], res_bin$sensitivity)