chorussell.eval: Computes the required object in the 'chorussell' procedure
In conroylau/lpinfer: An R Package for Inference in Linear Programs

chorussell.eval

R Documentation

Computes the required object in the `chorussell` procedure

Description

This function computes the required object in the chorussell procedure. If ci is TRUE, this function returns the (1-\alpha)-confidence interval. Otherwise, it returns the p-value.

Usage

chorussell.eval(
  beta.tgt,
  lb.can1,
  lb.can2,
  ub.can1,
  ub.can2,
  n,
  R,
  ci,
  alpha,
  tol,
  ub,
  lb,
  logical.ub,
  logical.lb,
  remove.const,
  kappa,
  progress,
  df.lb = NULL,
  df.ub = NULL
)

Arguments

`beta.tgt`	The value to be tested.
`lb.can1`	The vector of values that corresponds to `\sqrt{n}\left(\hat{\theta}^b_{\rm lb} - \hat{\theta}_{\rm lb}\right)`.
`lb.can2`	The vector of values that corresponds to `\sqrt{n}\left(\hat{\theta}^b_{\rm lb} - \hat{\theta}_{\rm lb} - \Delta \right)`.
`ub.can1`	The vector of values that corresponds to `\sqrt{n}\left(\hat{\theta}^b_{\rm ub} - \hat{\theta}_{\rm ub}\right)`.
`ub.can2`	The vector of values that corresponds to `\sqrt{n}\left(\hat{\theta}^b_{\rm ub} - \hat{\theta}_{\rm ub} + \Delta \right)`.
`n`	The sample size. This is only required if `data` is omitted in the input.
`R`	The number of bootstrap replications.
`ci`	A boolean variable that indicates whether a `p`-value or a `(1-\alpha)`-confidence interval is returned. If `ci` is `TRUE`, then a confidence interval is returned. Otherwise, a `p`-value is returned.
`alpha`	The significance level. This can be a vector.
`tol`	The tolerance level in the bisection procedure.
`ub`	The sample upper bound.
`lb`	The sample lower bound.
`logical.ub`	The logical upper bound.
`logical.lb`	The logical lower bound.
`remove.const`	A boolean variable. This determine whether the constraints are to be removed.
`kappa`	The tuning parameter used in the second step of the two-step procedure for obtaining the bounds subject to the shape constraints. It can be any nonnegative number or a vector of nonnegative numbers.
`progress`	The boolean variable for whether the progress bars should be displayed. If it is set as `TRUE`, the progress bars will be displayed while the code is running.
`df.lb`	The list of lower bounds that are obtained from the simplified program.
`df.ub`	The list of upper bounds that are obtained from the simplified program.