boot_ML {emery}

Bootstrap ML accuracy statistic estimation for multi-method data

Description

boot_ML() is a function used to generate bootstrap estimates of results generated by estimate_ML() primarily for use in creating nonparametric confidence intervals.

Usage

boot_ML(
  type = c("binary", "ordinal", "continuous"),
  data,
  n_boot = 100,
  max_iter = 1000,
  tol = 1e-07,
  seed = NULL,
  ...
)

Arguments

type

A string specifying the data type of the methods under evaluation.

data

An n_obs by n_method matrix containing the observed values for each method. If the dimensions are named, row names will be used to name each observation (obs_names) and column names will be used to name each measurement method (method_names).

n_boot

number of bootstrap estimates to compute

max_iter

The maximum number of EM algorithm iterations to compute before reporting a result.

tol

The minimum change in statistic estimates needed to continue iterating the EM algorithm.

seed

optional seed for RNG

...

Additional arguments

Value

a list containing accuracy estimates, v, and the parameters used.

v_0

result from original data

v_star

list containing results from each bootstrap resampling

params

list containing the parameters used

Examples

# Set seed for this example
set.seed(11001101)

# Generate data for 4 binary methods
my_sim <- generate_multimethod_data(
  "binary",
  n_obs = 75,
  n_method = 4,
  se = c(0.87, 0.92, 0.79, 0.95),
  sp = c(0.85, 0.93, 0.94, 0.80),
  method_names = c("alpha", "beta", "gamma", "delta"))

# Bootstrap ML results
boot_ex <- boot_ML(
  "binary",
  data = my_sim$generated_data,
  n_boot = 20)