fsst.check: Checks and updates the input in 'fsst'
In conroylau/lpinfer: An R Package for Inference in Linear Programs

fsst.check

R Documentation

Checks and updates the input in `fsst`

Description

This function checks and updates the input from the user in the fsst function. If there is any invalid input, the function will be terminated and error messages will be printed.

Usage

fsst.check(
  data,
  lpmodel,
  beta.tgt,
  R,
  Rmulti,
  lambda,
  rho,
  n,
  weight.matrix,
  solver,
  progress,
  sqrtm.method,
  sqrtm.tol,
  previous.output
)

Arguments

`data`	An `data frame` or a `matrix`.
`lpmodel`	The `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.
`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`.
`rho`	Parameter used in the studentization of matrices.
`n`	The sample size. This is only required if `data` is omitted in the input.
`weight.matrix`	The option used in the weighting matrix. There are three options available: `identity` — identity matrix `diag` — the diagonal matrix that takes the diagonal elements of the inverse of the variance matrix `avar` — inverse of the variance matrix
`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.
`sqrtm.method`	The method used to obtain the matrix square root in the `fsst` procedure. This has to be a function that takes one argument that accepts a square matrix of size k x k and returns a square matrix of size k x k, where k can be the length of the `\beta(P)` vector, or the `beta.obs` component of the `lpinfer` object.
`sqrtm.tol`	The absolute tolerance used to check whether the matrix square root is correct. This is done by checking whether the Frobenius norm is smaller than the tolerance level, i.e., when `A` is the give matrix, `B` is the matrix square root obtained from the given `sqrtm.method` function, and `\epsilon` is the tolerance level, the FSST test checks whether `\|\|A - BB\|\|_F < \epsilon`. If this does not hold, the FSST test will use the `sqrtm` function from the `expm` package to obtain the matrix square root.
`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.