RSAVS_Further_Improve: Post-selection estimation

In fenguoerbian/RSAVS: Robust Subgroup Analysis and Variable Selection

Post-selection estimation

Description

This function carries out the post-selection estimation given the model structure.

Usage

RSAVS_Further_Improve(
  y_vec,
  x_mat,
  l_type = "L1",
  l_param = NULL,
  mu_vec,
  beta_vec
)

Arguments

y_vec	numeric vector for response. n = length(y_vec) is the number of observations.
x_mat	numeric matrix for covariates. Each row is for one observation. p = ncol(x_mat) is the number of covariates.
l_type	character, type of loss function. "L1": L-1 loss. "L2": L-2 loss. "Huber": Huber loss. The default value is "1".
l_param	vector of parameters needed by the loss function. For Huber loss, c = l_param[1] and a popular choice is c = 1.345. The Default value is NULL since no additional parameters are needed for the default L-1 loss.
mu_vec	a length-n vector for subgroup effect. The function uses this to determine the subgroup structure.
beta_vec	a length-p vector for covariate effect. The function uses this to find the active covariates.

Details

This function uses mu_vec to determine the subgroup structure matrix via RSAVS_Mu_to_Mat. The active covariates are those with non-zero beta_vec entries.

Since this is the post-selection estimation. It's a regular estimate without any penalties. One should make sure the given model structure is identifiable.

Value

a list containing the improved mu(mu_vec) and beta(beta_vec) vector.

See Also

RSAVS_Mu_to_Mat for getting the subgroup index matrix from the subgroup effect vector.

fenguoerbian/RSAVS documentation built on Oct. 25, 2024, 3:16 p.m.