compute_support_paral {RegCombin}

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.

Value

the value of the support function in the specifed direction dir_nb.