compute_support_paral: Function to minimize to compute the function sigma for the...
In RegCombin: Partially Linear Regression under Data Combination

compute_support_paral

R Documentation

Function to minimize to compute the function sigma for the projections of the identified set

Description

Function to minimize to compute the function sigma for the projections of the identified set

Usage

compute_support_paral(
  dir_nb,
  sam0,
  Xnc,
  eps_default0,
  grid,
  dimXc,
  dimXnc,
  Xc_xb = NULL,
  Xncb,
  Xc_yb = NULL,
  Yb,
  values,
  weights_x,
  weights_y,
  constraint = NULL,
  c_sign,
  nc_sign,
  refs0,
  meth,
  T_xy,
  bc,
  version,
  R2bound = NULL,
  values_sel = NULL,
  ties = FALSE,
  modeNA = FALSE
)

Arguments

`dir_nb`	the reference for the considered direction e in sam0
`sam0`	the directions q to compute the radial function.
`Xnc`	the noncommon regressor on the dataset (Xnc,Xc). No default
`eps_default0`	the matrix containing the directions q and the selected epsilon(q)
`grid`	the number of points for the grid search on epsilon. Default is 30. If NULL, then epsilon is taken fixed equal to kp.
`dimXc`	the dimension of the common regressors Xc.
`dimXnc`	the dimension of the noncommon regressors Xnc.
`Xc_xb`	the possibly bootstraped/subsampled common regressor on the dataset (Xnc,Xc). Default is NULL.
`Xncb`	the possibly bootstraped/subsampled noncommon regressor on the dataset (Xnc,Xc). No default.
`Xc_yb`	the possibly bootstraped/subsampled common regressor on the dataset (Y,Xc). Default is NULL.
`Yb`	the possibly bootstraped/subsampled outcome variable on the dataset (Y,Xc). No default.
`values`	the different unique points of support of the common regressor Xc.
`weights_x`	the bootstrap or sampling weights for the dataset (Xnc,Xc).
`weights_y`	the bootstrap or sampling weights for the dataset (Y,Xc).
`constraint`	a vector indicating the different constraints in a vector of the size of X_c indicating the type of constraints, if any on f(X_c) : "concave", "concave", "nondecreasing", "nonincreasing", "nondecreasing_convex", "nondecreasing_concave", "nonincreasing_convex", "nonincreasing_concave", or NULL for none. Default is NULL, no contraints at all.#' @param nc_sign if sign restrictions on the non-commonly observed regressors Xnc: -1 for a minus sign, 1 for a plus sign, 0 otherwise. Default is NULL, i.e. no constraints.
`c_sign`	sign restrictions on the commonly observed regressors: -1 for a minus sign, 1 for a plus sign, 0 otherwise. Default is NULL, i.e. no constraints.
`nc_sign`	sign restrictions on the non-commonly observed regressors Xnc: -1 for a minus sign, 1 for a plus sign, 0 otherwise. Default is NULL, i.e. no constraints.
`refs0`	indicating the positions in the vector values corresponding to the components of betac.
`meth`	the method for the choice of epsilon, either "adapt", i.e. adapted to the direction or "min" the minimum over the directions. Default is "adapt".
`T_xy`	the apparent sample size the taking into account the difference in the two datasets.
`bc`	if TRUE compute also the bounds on betac. Default is FALSE.
`version`	version of the computation of the ratio, "first" indicates no weights, no ties, same sizes of the two datasets; "second" otherwise. Default is "second".
`R2bound`	the lower bound on the R2 of the long regression if any. Default is NULL.
`values_sel`	the selected values of Xc for the conditioning. Default is NULL.
`ties`	Boolean indicating if there are ties in the dataset. Default is FALSE.
`modeNA`	indicates if NA introduced if the interval is empty. Default is FALSE.