HDlars: Lars algorithm
In HDPenReg: High-Dimensional Penalized Regression

Description Usage Arguments Details Value Author(s) References See Also Examples

It performs the lars algorithm for solving lasso problem. It is a linear regression problem with a l1-penalty on the estimated coefficient.

1 2	HDlars(X, y, maxSteps = 3 * min(dim(X)), intercept = TRUE, eps = .Machine$double.eps^0.5)

`X`	the matrix (of size n*p) of the covariates.
`y`	a vector of length n with the response.
`maxSteps`	Maximal number of steps for lars algorithm.
`intercept`	If TRUE, add an intercept to the model.
`eps`	Tolerance of the algorithm.

The l1 penalty performs variable selection via shrinkage of the estimated coefficient. It depends on a penalty parameter called lambda controlling the amount of regularization. The objective function of lasso is :

||y-Xβ||_2 + λ||β||_1

An object of type LarsPath.

Quentin Grimonprez

Efron, Hastie, Johnstone and Tibshirani (2003) "Least Angle Regression" (with discussion) Annals of Statistics

LarsPath HDcvlars listToMatrix

dataset=simul(50,10000,0.4,10,50,matrix(c(0.1,0.8,0.02,0.02),nrow=2))
result=HDlars(dataset$data,dataset$response)
# Obtain estimated coefficient in matrix format
coefficient = listToMatrix(result)