fsst.cone.bs: Computes the bootstrap estimates of the cone component of the...
In conroylau/lpinfer: An R Package for Inference in Linear Programs

fsst.cone.bs

R Documentation

Computes the bootstrap estimates of the cone component of the test statistics

Description

This function computes the bootstrap estimates of the cone components.

Usage

fsst.cone.bs(
  n,
  omega.i,
  beta.n,
  beta.star,
  lpmodel,
  R.succ,
  lambda,
  indicator,
  beta.r,
  beta.star.list,
  solver,
  progress,
  df.error,
  eval.count,
  data.driven = FALSE
)

Arguments

`n`	The sample size. This is only required if `data` is omitted in the input.
`omega.i`	The matrix `\widehat{\bm{\Omega}}^i_n`, i.e. the regularized matrix for `\widehat{\bm{\Sigma}}^{\beta^\star}_{n,\bar{\rho}}`.
`beta.n`	The sample `\widehat{\bm{\beta}}_n` vector that is defined as `\widehat{\bm{\beta}}_n \equiv (\widehat{\bm{\beta}}_{{\rm obs},n}, \bm{\beta}_{{\rm shp},n}, \bm{\beta}_{{\rm tgt}})'`.
`beta.star`	The starred version of the `beta.n` vector, i.e. `\widehat{\bm{\beta}}^\star_n`.
`lpmodel`	The `lpmodel` object.
`R.succ`	The number of successful bootstrap replications.
`lambda`	Parameter used to obtain the restricted estimator `\widehat{\bm{\beta}}^r_n`. A data-driven parameter `lambda` can be included if `NA` is included as part of the vector for `lambda`. For instance, if `lambda` is set as `c(0.1, NA)`, then both 0.1 and the data-driven `lambda` will be applied in the `fsst` test. The default is to use the data-driven `lambda`.
`indicator`	A binary variable that equals to 1 for `d \geq p` and equals to 0 for `d < p`.
`beta.r`	The restricted estimator.
`beta.star.list`	This corresponds to the bootstrap estimates of `beta.star`.
`solver`	The name of the linear and quadratic programming solver that is used to obtain the solution to linear and quadratic programs. The solvers supported by this package are `cplexAPI`, `gurobi`, `limSolve` and `Rcplex`.
`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.error`	A table showing the id of the bootstrap replication(s) with error(s) and the corresponding error message(s).
`eval.count`	The count for the number of times the `future_map` function has been called. If this object is zero, it means that the `future_map` function is being called for the first time in this subprocedure. Otherwise, it means that the `future_map` function has been called for more than once. This situation typically refers to the situations where there are some errors in the first time of the replications.
`data.driven`	A boolean variable. This indicates whether the data-driven problem is being considered.

Value

Return the following list of objects:

`cone.n.list`	The list of bootstrap cone components.
`new.error`	The number of new errors.
`df.error`	Table showing the id of the bootstrap replication(s) with error(s) and the corresponding error message(s).
`error.id`	An indices of the bootstrap replications that give errors.
`R.succ`	A number of successful bootstrap replications.