boot_estimate_valid: Bootstrapping with a progress bar

In mcbeem/giftedCalcs: Psychometrics for Gifted Program Identification

Bootstrapping with a progress bar

Description

Bootstrapping with a progress bar

Usage

boot_estimate_valid(
  reps,
  x,
  nom.rate,
  id.rate,
  pop.mean = 0,
  pop.sd = 1,
  adjust = 1
)

Arguments

reps	Number of bootstrapped repetitions.
x	Numeric vector of observed scores.
nom.rate	Nomination rate. Must be between 0 and 1.
id.rate	Identification rate. Must be between 0 and 1.
pop.mean	The known general population mean of x. Defaults to 0.
pop.sd	The known general population standard deviation of x. Defaults to 1.
adjust	Controls the bandwidth of the density estimator. Defaults to 1.0, which has been found to perform well in simulation.

Examples

# generate some observed scores
# (note the lack of a relyt argument)
# true validity is .6
set.seed(1)
x <- r_identified(
  n = 500, test.cutoff = .9, valid = .6,
  nom.cutoff = .85
)

# calculate the identification rate implied by the system parameters
id.rate <- marginal_psychometrics(
  test.cutoff = .9, valid = .6,
  nom.cutoff = .85
)$identification.rate

# calculate the nomination rate implied by the system parameters
nom.rate <- marginal_psychometrics(
  test.cutoff = .9, valid = .6,
  nom.cutoff = .85
)$nom.rate

# estimate the nomination validity with 10 bootstrapped samples
boot_estimate_valid(x, id.rate = id.rate, nom.rate = nom.rate, reps = 10)

mcbeem/giftedCalcs documentation built on May 3, 2022, 3:34 a.m.