stapler (version 0.8.0)

staple: Generic STAPLE Algorithm

Description

Tries to do the correct STAPLE algorithm (binary/multi-class) for the type of input (array/matrix/list of images/filenames of images)

Usage

staple(x, ..., set_orient = FALSE)
# S3 method for default
staple(x, ..., set_orient = FALSE)
# S3 method for list
staple(x, ..., set_orient = FALSE)
# S3 method for character
staple(x, ..., set_orient = FALSE)
# S3 method for array
staple(x, ..., set_orient = FALSE)

Arguments

x

a nxr matrix where there are n raters and r elements rated, a list of images, or a character vector. Note, readNifti is used for image filenames

...

Options for STAPLE, see staple_bin_mat and staple_multi_mat

set_orient

Should the orientation be set to the same if x is a set of images, including niftiImages.

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(x)
print(staple_out$sensitivity)
if (is.matrix(staple_out$sensitivity)) {
   staple_out$sensitivity = staple_out$sensitivity[, "1"]
}
testthat::expect_equal(staple_out$sensitivity,
c(0.781593858553476, 0.895868301462594,
0.760514086161722, 0.464483444340873,
0.765239314719065))
staple_out_prior = staple(x, prior = rep(0.5, r))

if (is.matrix(staple_out_prior$sensitivity)) {
   staple_out_prior$sensitivity = staple_out_prior$sensitivity[, "1"]
}
testthat::expect_equal(staple_out_prior$sensitivity,
c(0.683572080864211, 0.821556768891859,
0.619166852992802, 0.389409921992467, 0.67042085955546))

res_bin = staple_bin_mat(x, prior = rep(0.5, 1000))
testthat::expect_equal(staple_out_prior$sensitivity,
res_bin$sensitivity)
n = 5
r = 1000
x = lapply(seq(n), function(i) {
   x = rbinom(n = r, size = 1, prob = 0.5)
   array(x, dim = c(10,10, 10))
 })
mat = sapply(x, c)
staple_out = staple_bin_img(x, set_orient = FALSE)
res_mat = staple(t(mat))
if (is.matrix(res_mat$sensitivity)) {
   res_mat$sensitivity = res_mat$sensitivity[, "1"]
}
testthat::expect_equal(staple_out$sensitivity, res_mat$sensitivity)