HDlars {HDPenReg} | R Documentation |
It performs the lars algorithm for solving lasso problem. It is a linear regression problem with a l1-penalty on the estimated coefficient.
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)