Description Usage Arguments Value
View source: R/cv_covariance_matrices_block_descent_general.R
Creating projected nearest positive semi-definite covariance matrices for the cross validation step of the BD-CoCoLasso algorithm. In that case, the design matrix must be organized as follozs : uncorrupted features must be the first block of the matrix, and corrupted features must be the second block of the matrix.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | cv_covariance_matrices_block_descent_general(
K,
mat,
y,
p,
p1,
p2,
p3,
mu,
tau = NULL,
ratio_matrix = NULL,
etol = 1e-04,
mode = "ADMM"
)
|
K |
Number of folds for the cross validation |
mat |
Covariance matrix to be projected |
y |
Response vector |
p |
Number of predictors |
p1 |
Number of uncorrupted predictors |
p2 |
Number of corrupted predictors containing additive error |
p3 |
Number of corrupted predictors containing missingness |
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 |
mode |
ADMM or HM |
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
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