subsample.bs.fn: Carries out one bootstrap replication for the subsampling...
In conroylau/lpinfer: An R Package for Inference in Linear Programs

subsample.bs.fn

R Documentation

Carries out one bootstrap replication for the subsampling test

Description

This function carries out the one bootstrap replication of the subsampling test. This function is used in the subsample.bs function via the future_map function.

Usage

subsample.bs.fn(
  x,
  data,
  lpmodel,
  beta.tgt,
  norm,
  m,
  solver,
  replace,
  n,
  pbar,
  eval.count,
  n.bs,
  any.list,
  progress,
  omega.hat
)

Arguments

`x`	This is either the list of indices that represent the bootstrap replications, or the list of bootstrap components of the `lpmodel` object passed from the user.
`data`	An `data frame` or a `matrix`.
`lpmodel`	An `lpmodel` object.
`beta.tgt`	The value to be tested.
`norm`	The norm used in the optimization problem. It can be either a 1-norm or a 2-norm. See the details section of `estbounds` for a list of accepted strings.
`m`	The size of each subsample.
`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`.
`replace`	A boolean variable to indicate whether the function samples the data with or without replacement.
`n`	The sample size. This is only required if `data` is omitted in the input.
`pbar`	The progress bar object.
`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.
`n.bs`	The total number of replications to be conducted in this procedure.
`any.list`	This object contains information on whether any components in the `lpmodel` object is a `list`.
`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.
`omega.hat`	The estimator of the asymptotic variance.