dkqs: Conducts inference using the DKQS procedure
In conroylau/lpinfer: An R Package for Inference in Linear Programs

View source: R/dkqs.R

dkqs	R Documentation

Conducts inference using the DKQS procedure

Description

This module conducts inference using the cone-tightening procedure proposed by Deb, Kitamura, Quah and Stoye (2018).

Usage

dkqs(
  data = NULL,
  lpmodel,
  beta.tgt,
  R = 100,
  Rmulti = 1.25,
  tau = NULL,
  n = NULL,
  solver = NULL,
  progress = TRUE,
  previous.output = NA
)

Arguments

`data`	An `data frame` or a `matrix`.
`lpmodel`	An `lpmodel` object.
`beta.tgt`	The value to be tested.
`R`	The number of bootstrap replications.
`Rmulti`	The multiplier for the number of bootstrap replications. The product of `Rmulti` and `R` refers to the maximum number of bootstrap replications to be conducted if there are errors.
`tau`	The value of the tuning parameter `\tau` in the DKQS procedure. This can be a vector.
`n`	The sample size. This is only required if `data` is omitted in the input.
`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.
`previous.output`	The list of outputs obtained from the previous evaluation of the procedure. This is only used in the `fsst` test and when `previous.out` contains the studentized matrix `omega.i`. The rest of the arguments are ignored.

Details

If the value of the test statistic T_n is zero, the bootstrap procedure will be skipped and the p-value is zero.
The following components are required in the lpmodel for the DKQS procedure:
- A.tgt
- A.obs
- beta.obs

Value

Returns the following list of outputs:

`pval`	A table of `p`-values for each `\tau`.
`tau.feasible`	The list of `\tau` that are feasible.
`tau.infeasible`	The list of `\tau` that are infeasible.
`tau.max`	The maximum value of the feasible `\tau` for the problem.
`T.n`	The test statistic `T_n`.
`T.bs`	The list of bootstrap test statistics `\{\overline{T}_{n,b}(\tau_n)\}^B_{b=1}` for each `\tau`.
`solver`	The solver used.
`cv.table`	A table of critical values.
`call`	The function that has been called.
`test.logical`	An indicator variable for whether the computation has been conducted. If `test.logical` is 1, it refers to the case where `beta.tgt` is inside the logical bounds. If `test.logical` is 0, it refers to the case where `beta.tgt` is outside the logical bounds.
`logical.lb`	The logical lower bound.
`logical.ub`	The logical upper bound.
`df.error`	A table showing the id of the bootstrap replication(s) with error(s) and the corresponding error message(s).
`R.succ`	The number of successful bootstrap replications.

Example

  source("./example/dgp_missingdata.R") # Change directory if necessary
  J <- 5
  N <- 1000
  data <- missingdata_draw(J = J, n = N, seed = 1, prob.obs = .5)
  lpm <- missingdata_lpm(J = J, info = "full", data = data)
  dkqs(data = data,
       lpmodel = lpm,
       beta.tgt = .2,
       R = 100,
       tau = sqrt(log(N)/N),
       solver = "gurobi")