Description Usage Arguments Value
View source: R/lasso_covariance_block_general.R
Solve the least squares loss with lasso penalty written in a form with the covariance matrix : \frac{1}{2} β^{'} Σ β - ρ^{'} β + λ \|β\|_1
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n |
Number of samples of the design matrix |
p1 |
Number of uncorrupted predictors |
p2 |
Number of corrupted predictors containing additive error |
p3 |
Number of corrupted predictors containing missingness |
X1 |
first block of the design matrix corresponding to uncorrupted features |
Z2 |
second block of the design matrix corresponding to corrupted features containing additive error |
Z3 |
third block of the design matrix corresponding to corrupted features containing missingness |
y |
Response vector |
sigma1 |
Covariance matrix for X1 : \frac{1}{n} X_1'X_1. This parameter is automatically furnished in blockwise_coordinate_descent |
sigma2 |
Modified covariance matrix for Z2 through the CoCoLasso algorithm. This parameter is automatically furnished in blockwise_coordinate_descent |
sigma3 |
Modified covariance matrix for Z3 through the CoCoLasso algorithm. This parameter is automatically furnished in blockwise_coordinate_descent |
lambda |
Penalty parameter |
ratio_matrix |
Observation matrix in the missing data setting (NULL otherwise) |
control |
Including control parameters : max of iterations, tolerance for the convergence of the error, zero threshold to put to zero small beta coefficients |
beta1.start |
Initial value for the coefficients of uncorrupted features |
beta2.start |
Initial value for the coefficients of corrupted features containing additive error |
beta3.start |
Initial value for the coefficients of corrupted features containing missingness |
penalty |
Type of penalty used : can be lasso penalty or SCAD penalty |
list containing
coefficients.beta1 : Coefficients corresponding to final beta1 after convergence of the algoritm
coefficients.beta2 : Coefficients corresponding to final beta2 after convergence of the algoritm
num.it : Number of iterations of algorithm
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