huber.lasso | R Documentation |
This function is L1 penalized Huber estimator for linear regression under both fixed and high-dimensional settings. Currently, the function does not support automatic selection of huber tuning parameter.
huber.lasso( X, y, beta.ini, lambda, alpha = 2, adaptive = TRUE, intercept = FALSE, penalty.factor = rep(1, ncol(X)) )
X |
design matrix, standardization is recommended. |
y |
response vector. |
beta.ini |
initial estimates of beta. If not specified, LADLasso estimates from |
lambda |
regularization parameter of Lasso or adaptive Lasso (if adaptive=TRUE). |
alpha |
1/alpha is the huber tuning parameter. Larger alpha results in smaller portion of squared loss. |
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. |
beta |
the regression coefficient estimates. |
fitted |
predicted response. |
iter.steps |
iteration steps. |
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.HuberLasso=huber.lasso(X,y, adaptive=TRUE) beta.est=output.HuberLasso$beta
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