| Version: | 0.8.0 |
| Title: | Simultaneous Truth and Performance Level Estimation |
| Description: | An implementation of Simultaneous Truth and Performance Level Estimation (STAPLE) <doi:10.1109/TMI.2004.828354>. This method is used when there are multiple raters for an object, typically an image, and this method fuses these ratings into one rating. It uses an expectation-maximization method to estimate this rating and the individual specificity/sensitivity for each rater. |
| License: | GPL-3 |
| Imports: | matrixStats, RNifti |
| Suggests: | knitr, rmarkdown, covr, testthat, spelling |
| Encoding: | UTF-8 |
| ByteCompile: | true |
| Type: | Package |
| Maintainer: | John Muschelli <muschellij2@gmail.com> |
| VignetteBuilder: | knitr |
| URL: | https://github.com/muschellij2/stapler |
| BugReports: | https://github.com/muschellij2/stapler/issues |
| RoxygenNote: | 7.3.2 |
| Language: | en-US |
| NeedsCompilation: | no |
| Packaged: | 2025-04-01 16:01:50 UTC; johnmuschelli |
| Author: | John Muschelli [aut, cre] |
| Repository: | CRAN |
| Date/Publication: | 2025-04-01 16:40:02 UTC |
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)
## Default S3 method:
staple(x, ..., set_orient = FALSE)
## S3 method for class 'list'
staple(x, ..., set_orient = FALSE)
## S3 method for class 'character'
staple(x, ..., set_orient = FALSE)
## S3 method for class '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, |
... |
Options for STAPLE, see |
set_orient |
Should the orientation be set to the same if x is a
set of images, including |
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)
Run STAPLE on a set of nifti images
Description
Run STAPLE on a set of nifti images
Usage
staple_bin_img(x, set_orient = FALSE, verbose = TRUE, ...)
staple_multi_img(x, set_orient = FALSE, verbose = TRUE, ...)
Arguments
x |
Character vector of filenames or list of arrays/images |
set_orient |
Should the orientation be set to the same if the images are
|
verbose |
print diagnostic messages |
... |
Additional arguments to |
Value
A list similar to staple_bin_mat, but
has a resulting image
Examples
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))
})
staple_out = staple_bin_img(x, set_orient = FALSE)
res = staple(x)
testthat::expect_equal(staple_out$sensitivity,
res$sensitivity)
x = lapply(x, RNifti::asNifti, internal = FALSE)
staple_img_out = staple_bin_img(x, set_orient = FALSE)
testthat::expect_equal(staple_out$sensitivity,
staple_img_out$sensitivity)
n = 5
r = 1000
x = lapply(seq(n), function(i) {
x = rbinom(n = r, size = 5, prob = 0.5)
array(x, dim = c(10,10, 10))
})
staple_out = staple_multi_img(x, set_orient = FALSE)
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$sensitivity,
c(0.683572080864211, 0.821556768891859,
0.619166852992802, 0.389409921992467, 0.67042085955546))
STAPLE Example Data
Description
STAPLE Example Data
Usage
staple_example_data()
Value
Character vector of filenames
Examples
staple_example_data()
STAPLE on Multi-class matrix
Description
STAPLE on Multi-class matrix
Usage
staple_multi_mat(
x,
sens_init = 0.99999,
spec_init = 0.99999,
max_iter = 10000,
tol = .Machine$double.eps,
prior = "mean",
verbose = TRUE,
trace = 25,
ties.method = c("first", "random", "last"),
drop_all_same = FALSE
)
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 |
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)