fusedLasso {smoothedLasso} | R Documentation |
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the fused Lasso.
fusedLasso(X, y, E, lambda, gamma)
X |
The design matrix. |
y |
The response vector. |
E |
The adjacency matrix which encodes with a one in position |
lambda |
The first regularization parameter of the fused Lasso. |
gamma |
The second regularization parameter of the fused Lasso. |
A list with six functions, precisely the objective u
, penalty v
, and dependence structure w
, as well as their derivatives du
, dv
, and dw
.
Tibshirani, R., Saunders, M., Rosset, S., Zhu, J., and Knight, K. (2005). Sparsity and Smoothness via the Fused Lasso. J Roy Stat Soc B Met, 67(1):91-108.
Arnold, T.B. and Tibshirani, R.J. (2020). genlasso: Path Algorithm for Generalized Lasso Problems. R package version 1.5.
Hahn, G., Lutz, S., Laha, N., and Lange, C. (2020). A framework to efficiently smooth L1 penalties for linear regression. bioRxiv:2020.09.17.301788.
library(smoothedLasso)
n <- 100
p <- 500
betavector <- runif(p)
X <- matrix(runif(n*p),nrow=n,ncol=p)
y <- X %*% betavector
E <- matrix(sample(c(TRUE,FALSE),p*p,replace=TRUE),p)
lambda <- 1
gamma <- 0.5
temp <- fusedLasso(X,y,E,lambda,gamma)