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Last data update: 2014.03.03

R: Lars algorithm

HDlars

R Documentation

Lars algorithm

Description

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

Usage

HDlars(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, add an intercept to the model.
`eps`	Tolerance of the algorithm.

Details

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

Value

An object of type LarsPath.

Author(s)

Quentin Grimonprez

References

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

Examples

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(HDPenReg)
Loading required package: rtkore
Loading required package: Rcpp

Attaching package: 'rtkore'

The following object is masked from 'package:Rcpp':

    LdFlags

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/HDPenReg/HDlars.Rd_%03d_medium.png", width=480, height=480)
> ### Name: HDlars
> ### Title: Lars algorithm
> ### Aliases: HDlars
> 
> ### ** Examples
> 
> 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)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>