rankflassopath: rank fused Lasso regression

In Mufabo/Rrobustsp: Robust Signal Processing

rank fused Lasso regression

Description

Computes the rank fused-Lasso regression estimates for given fused penalty value lambda_2 and for a range of lambda_1 values

Usage

rankflassopath(y, X, lambda2, L = 120, eps = 0.001, printitn = F)

Arguments

y	: numeric response N x 1 vector (real/complex)
X	: numeric feature N x p matrix (real/complex)
lambda2	: positive penalty parameter for the fused Lasso penalty term
L	: number of grid points for lambda1 (Lasso penalty)
eps	: Positive scalar, the ratio of the smallest to the largest Lambda value in the grid. Default is eps = 10^-4.
printitn	: print iteration number (default = F, no printing)

Value

B: Fitted rank fused-Lasso regression coefficients, a p-by-(L+1) matrix, where p is the number of predictors (columns) in X, and L is the number of Lambda values.

B0: estimates values of intercepts

lamgrid: = lambda parameters

Examples

rankflassopath()

Mufabo/Rrobustsp documentation built on June 11, 2022, 10:41 p.m.