cv_covariance_matrices: Projected covariance matrices

In celiaescribe/BDcocolasso: Implementation of CoCoLasso and Block Descent CoCoLasso

Description

Creating projected nearest positive semi-definite covariance matrices for the cross validation step of the CoCoLasso

Usage

cv_covariance_matrices(
  K,
  mat,
  y,
  p,
  mu,
  tau = NULL,
  ratio_matrix = NULL,
  etol = 1e-04,
  noise = c("additive", "missing"),
  mode = "ADMM"
)

Arguments

K	Number of folds for the cross validation
mat	Covariance matrix to be projected
y	Response vector
p	Number of predictors
mu	Penalty parameter for the ADMM algorithm.
tau	Standard error of the additive error matrix when the chosen setting is the additive error setting
ratio_matrix	Observation matrix used in the missing data setting
etol	Tolerance used in the ADMM algorithm
noise	Type of setting chosen : additive or missing
mode	ADMM or HM

Value

list containing

• sigma_global projected matrix for mat

• rho_global rho parameter for mat

• list_matrices_lasso list of the projected matrices for mat deprived of the k-fold during cross validation

• list_matrices_error list of the projected matrices for the k-fold of mat

• list_rho_lasso list of the modified rho for mat deprived of the K-fold during cross validation

• list_rho_error list of the modified rho for the k-fold of mat

celiaescribe/BDcocolasso documentation built on Feb. 11, 2020, 11:41 p.m.