LassoSIR (version 1.0)

LassoSIR-Package: tools:::Rd_package_title("LassoSIR")

Description

tools:::Rd_package_description("LassoSIR")

Author

tools:::Rd_package_author("LassoSIR")

Maintainer: tools:::Rd_package_maintainer("LassoSIR")

Details

The DESCRIPTION file: tools:::Rd_package_DESCRIPTION("LassoSIR") tools:::Rd_package_indices("LassoSIR") LassoSIR

References

Lin, Q., Zhao, Z. , and Liu, J. (2018) On consistency and sparsity for sliced inverse regression in high dimension. Annals of Statistics. Vol. 46, Number 2. Page 580-610.

Lin, Q., Zhao, Z. , and Liu, J. (2019) Sparse Sliced Inverse Regression for High Dimensional Data. Journal of the American Statistical Association. Vol. 114, Number 528, Page 1726-1739.

See Also

NA

Examples

p <- 10
n <- 200


H <- 20
m <- n/H

beta <- array(0, c(p, 1) )
beta[1:3,1] <- rnorm(3, 0, 1)

X <- array(0, c(n, p ) )

rho <- 0.3
Sigma <- diag(p)
elements <- rho^(c((p-1):0,1:(p-1) ) )
for(i in 1:p)
    Sigma[i,] <- elements[(p+1-i):(2*p-i) ]


X <- matrix( rnorm(p*n), c(n, p) )
X <- X%*% chol(Sigma)

Y <-  ( X%*% beta )^3/2 + rnorm(n,0,1)
sir.lasso <- LassoSIR( X, Y, H, choosing.d="automatic",
          solution.path=FALSE, categorical=FALSE, nfolds=10,
          screening=FALSE)
beta.hat <- sir.lasso$beta/sqrt( sum( sir.lasso$beta^2 ) )