predict_Lasso_SIR: Prediction based on Lasso SIR

In LassoSIR: Sparsed Sliced Inverse Regression via Lasso

Prediction based on Lasso SIR

Description

This function calculates the value of X\beta for a new data set.

Usage

predict_Lasso_SIR( lassosirobj, newdata )

Arguments

lassosirobj	LassoSIR object when running the function LassoSIR.
newdata	A data frame consisting of the values of the predictors.

Details

Based on the estiamted \beta, this function provides the value X\beta for any new input.

Value

predict_value = predict_value, beta = lassosirobj$beta, no.dim = lassosirobj$no.dim

predict_value	the value of X\beta.
beta	the estiamted value of the \beta.
no.dim	the dimension of the central space.

Author(s)

Zhigen Zhao, Qian Lin, Jun S. Liu

References

Lin, Q., Zhao, Z. , and Liu, J. (2017) On consistency and sparsity for sliced inverse regression in high dimension. Annals of Statistics.

Lin, Q., Zhao, Z. , and Liu, J. (2016) Sparse Sliced Inverse Regression for High Dimensional Data.

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)

res = predict_Lasso_SIR( sir.lasso, newdata=data.frame( matrix( rnorm(5*p), c(5, p) )  ) )

LassoSIR documentation built on April 3, 2025, 8:49 p.m.