HDfusion: Fusion algorithm

In HDPenReg: High-Dimensional Penalized Regression

Description

It performs the lars algorithm for solving a special case of lasso problem. It is a linear regression problem with a l1-penalty on the difference of two successive coefficients.

Usage

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

Arguments

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, there is an intercept in the model.
eps	Tolerance of the algorithm.

Value

An object of type LarsPath. LarsPath-class.

Author(s)

Quentin Grimonprez

References

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

See Also

LarsPath HDlars

Examples

 
dataset=simul(50,10000,0.4,10,50,matrix(c(0.1,0.8,0.02,0.02),nrow=2))
result=HDfusion(dataset$data,dataset$response)

HDPenReg documentation built on May 2, 2019, 6:09 p.m.