LADlasso {MTE} | R Documentation |
LAD-Lasso for Linear Regression
Description
LAD-Lasso for Linear Regression
Usage
LADlasso(
X,
y,
beta.ini,
lambda = NULL,
adaptive = TRUE,
intercept = FALSE,
penalty.factor = rep(1, ncol(X))
)
Arguments
X |
design matrix, standardization is recommended. |
y |
reponse vector |
beta.ini |
initial estimates of beta. Using unpenalized LAD is recommended under high-dimensional setting. |
lambda |
regularization parameter of Lasso or adaptive Lasso (if adaptive=TRUE). |
adaptive |
logical input that indicates if adaptive Lasso is used. Default is TRUE. |
intercept |
logical input that indicates if intercept needs to be estimated. Default is FALSE. |
penalty.factor |
can be used to force nonzero coefficients. Default is rep(1, ncol(X)) as in glmnet. |
Value
beta |
the regression coefficient estimates. |
fitted |
predicted response. |
iter.steps |
iteration steps. |
Examples
set.seed(2017)
n=200; d=50
X=matrix(rnorm(n*d), nrow=n, ncol=d)
beta=c(rep(2,6), rep(0, 44))
y=X%*%beta+c(rnorm(150), rnorm(30,10,10), rnorm(20,0,100))
output.LADLasso=LADlasso(X, y, beta.ini=LAD(X, y))
beta.est=output.LADLasso$beta
[Package MTE version 1.2 Index]