covlasso: Robust adaptive lasso based on robust correlation estimates

In PengSU517/robcovsel: Robust variable selection based on covariance matrix

Robust adaptive lasso based on robust correlation estimates

Description

Robust adaptive lasso based on robust correlation estimates

Usage

covlasso(
  x,
  y,
  cor.method = "gaussrank",
  scale.method = "qn",
  center.method = "median",
  adaptive = TRUE
)

Arguments

x	input design matrix
y	input response vector
cor.method	could be "pearson" or "gaussrank"
scale.method	"sd" or "qn"
adaptive	adaptive regularization penalties
std	If TRUE the robust correlation matrix is used, if FALSE the robust covariance matrix is used.
cormatrix	you could also use a correlation matrix as input
scale	put in scales if you use cormatrix

Value

betahat_opt, the optimal estimation of beta obtained from this algorithm

lambda_opt is the optimal tuning parameter and sigma_opt is the optimal estimation of sigma.

The output also includes the estimated correlation matrix, the estimated covariance matrix and et cetera from the covf function.

Examples

dat = genevar()
y = dat$y
x = dat$x
fit = covlasso(x,y)
fit$betahat_opt

PengSU517/robcovsel documentation built on Sept. 13, 2023, 1 a.m.