subsample.prob: Formulates and solves the 'subsample' problem

In conroylau/lpinfer: An R Package for Inference in Linear Programs

Formulates and solves the subsample problem

Description

This function formulates and solves the linear or quadratic program in the subsample procedure. If the user chooses a 1-norm, this function solves a linear program. If the user chooses a 2-norm, this function solves a quadratic program.

Usage

subsample.prob(
  data,
  lpmodel,
  beta.tgt,
  norm,
  solver,
  n,
  beta.obs.hat,
  omega.hat
)

Arguments

data	An data frame or a matrix.
lpmodel	The lpmodel object used in the test.
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.
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.
n	The sample size. This is only required if data is omitted in the input.
beta.obs.hat	The value of sample \hat{\bm{\beta}}_{\mathrm{obs}} from the lpmodel object.
omega.hat	The estimator of the asymptotic variance.

Value

Returns the following list of outputs:

status	The status of the optimization problem.
x	The optimal point.
objval	The optimal value.
larg	The list of arguments passed to the optimizer.
beta	The beta vector \widehat{\bm{\beta}}_{\mathrm{obs}} used in the optimization problem that is obtained from the beta.obs component of the lpmodel object.
omega	The Omega matrix \widehat{\bm{\Omega}}_n used in the optimization problem that is obtained from the beta.obs component of the lpmodel object.

conroylau/lpinfer documentation built on Oct. 25, 2024, 4 a.m.