compute_support: Compute the support function for the projections of the identified set

Description

Compute the support function for the projections of the identified set

Usage

compute_support(
  sample1 = NULL,
  Xc_x,
  Xnc,
  Xc_y,
  Y,
  values,
  dimXc,
  dimXnc,
  nb_pts,
  sam0,
  eps_default0,
  grid,
  lim = 30,
  weights_x = NULL,
  weights_y = NULL,
  constraint = NULL,
  c_sign = NULL,
  nc_sign = NULL,
  refs0 = NULL,
  type = "both",
  meth = "adapt",
  bc = FALSE,
  version = "first",
  R2bound = NULL,
  values_sel = NULL,
  ties = FALSE,
  modeNA = FALSE
)

Value

a matrix containing the considered directions and the computed value of the support function.

Arguments

sample1: if NULL compute the point estimate, if a natural number then evaluate a bootstrap or subsampling replication.
Xc_x: the common regressor on the dataset (Xnc,Xc). Default is NULL.
Xnc: the noncommon regressor on the dataset (Xnc,Xc). No default.
Xc_y: the common regressor on the dataset (Y,Xc). Default is NULL.
Y: the outcome variable. No default.
values: the different unique points of support of the common regressor Xc.
dimXc: the dimension of the common regressors Xc.
dimXnc: the dimension of the noncommon regressors Xnc.
nb_pts: the constant C in DGM for the epsilon_0, the lower bound on the grid for epsilon, taken equal to nb_pts*ln(n)/n. Default is 1 without regressors Xc, 3 with Xc.
sam0: the directions q to compute the variance bounds on the radial function.
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.
lim: the limit number of observations under which we do no compute the conditional variance.
weights_x: the sampling weights for the dataset (Xnc,Xc).
weights_y: the 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.
type: Equal to "both".
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".
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.