R: STAPLE on binary matrix

staple_bin_mat {stapler} | R Documentation

## 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 = 25)

### 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

### 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)
    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)
    staple_out_prior = staple_bin_mat(x, prior = rep(0.5, r))

[Package stapler version 0.6.5 Index]