Package stapler. November 27, 2017
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1 Version Package stapler November 27, 2017 Title Simultaneous Truth and Performance Level Estimation An implementation of Simultaneous Truth and Performance Level Estimation (STAPLE) <doi: /tmi >. 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 epectation-maimization method to estimate this rating and the individual specificity/sensitivity for each rater. License GPL-3 Imports matristats, RNifti Suggests knitr, rmarkdown, covr, testthat Encoding UTF-8 LazyData true ByteCompile true Type Package Maintainer John Muschelli VignetteBuilder knitr URL BugReports RoygenNote NeedsCompilation no Author John Muschelli [aut, cre] Repository CRAN Date/Publication :45:56 UTC R topics documented: staple staple_bin_img
2 2 staple staple_bin_mat staple_eample_data staple_multi_mat Inde 7 staple Generic STAPLE Algorithm Tries to do the correct STAPLE algorithm (binary/multi-class) for the type of input (array/matri/list of images/filenames of images) ## Default S3 method: ## S3 method for class 'list' ## S3 method for class 'character' ## S3 method for class 'array' a nr matri 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 is a set of images, including niftiimages.
3 staple_bin_img 3 staple_bin_img Run STAPLE on a set of nifti images Run STAPLE on a set of nifti images staple_bin_img(, set_orient = FALSE, verbose = TRUE,...) staple_multi_img(, set_orient = FALSE, verbose = TRUE,...) Character vector of filenames or list of arrays/images set_orient Should the orientation be set to the same if the images are niftiimages verbose print diagnostic messages... Additional arguments to staple_bin_mat A list similar to staple_bin_mat, but has a resulting image n = 5 r = 1000 = lapply(seq(n), function(i) { = rbinom(n = r, size = 1, prob = 0.5) array(, dim = c(10,10, 10)) staple_out = staple_bin_img(, set_orient = FALSE) n = 5 r = 1000 = lapply(seq(n), function(i) { = rbinom(n = r, size = 5, prob = 0.5) array(, dim = c(10,10, 10)) staple_out = staple_multi_img(, set_orient = FALSE)
4 4 staple_bin_mat staple_bin_mat STAPLE on binary matri STAPLE on binary matri staple_bin_mat(, sens_init = , spec_init = , ma_iter = 10000, tol =.Machine$double.eps, prior = "mean", verbose = TRUE, trace = 25) sens_init spec_init ma_iter tol prior verbose trace a nr matri where there are n raters and r elements rated Initialize parameter for sensitivity (p) Initialize parameter for specificity (q) Maimum number of iterations to run Tolerance for convergence Either "mean" or a vector of prior probabilities, print diagnostic messages Number for modulus to print out verbose iterations List of output sensitivities, specificities, and vector of probabilities n = 5 r = 1000 sens = c(0.8, 0.9, 0.8, 0.5, 0.8) spec = c(0.9, 0.75, 0.99, 0.98, 0.92) set.seed( ) n_1 = 200 n_0 = r - n_1 truth = c(rep(0, n_0), rep(1, n_1)) pred_1 = rbinom(n = n, size = n_1, prob = sens) pred_0 = rbinom(n = n, size = n_0, prob = spec) pred_0 = sapply(pred_0, function(n) { sample(c(rep(0, n), rep(1, n_0 -n))) pred_1 = sapply(pred_1, function(n) { sample(c(rep(1, n), rep(0, n_1 -n))) pred = rbind(pred_0, pred_1)
5 staple_eample_data 5 true_sens = colmeans(pred[ truth == 1, ]) true_spec = colmeans(1-pred[ truth == 0, ]) = t(pred) staple_out = staple_bin_mat() staple_out_prior = staple_bin_mat(, prior = rep(0.5, r)) staple_eample_data STAPLE Eample Data STAPLE Eample Data staple_eample_data() Character vector of filenames staple_eample_data() staple_multi_mat STAPLE on Multi-class matri STAPLE on Multi-class matri staple_multi_mat(, sens_init = , spec_init = , ma_iter = 10000, tol =.Machine$double.eps, prior = "mean", verbose = TRUE, trace = 25, ties.method = c("first", "random", "last"))
6 6 staple_multi_mat sens_init spec_init ma_iter tol prior verbose trace ties.method a nr matri where there are n raters and r elements rated Initialize matri for sensitivity (p) Initialize matri for specificity (q) Maimum number of iterations to run Tolerance for convergence Either "mean" or a matri of prior probabilities, print diagnostic messages Number for modulus to print out verbose iterations Method passed to ma.col for hard segmentation List of matri output sensitivities, specificities, and matri of probabilities rm(list = ls()) = matri(rbinom(5000, size = 5, prob = 0.5), ncol = 1000) sens_init = spec_init = ma_iter = tol =.Machine$double.eps prior = "mean" verbose = TRUE trace = 25 ties.method = "first" res = staple_multi_mat()
7 Inde ma.col, 6 readnifti, 2 staple, 2 staple_bin_img, 3 staple_bin_mat, 2, 3, 4 staple_eample_data, 5 staple_multi_img (staple_bin_img), 3 staple_multi_mat, 2, 5 7
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