boot.xenv: Bootstrap for xenv

In therimalaya/envelope: Computing Envelope Estimators in Multivariate Linear Regression

Description

Compute bootstrap standard error for the envelope estimator.

Usage

boot.xenv(X, Y, u, B)

Arguments

X	Predictors. An n by p matrix, p is the number of predictors and n is number of observations. The predictors must be continuous variables.
Y	Responses. An n by r matrix, r is the number of responses. The response can be univariate or multivariate and must be continuous variable.
u	Dimension of the envelope. An integer between 0 and p.
B	Number of bootstrap samples. A positive integer.

Details

This function computes the bootstrap standard errors for the regression coefficients in the envelope model in predictor space by bootstrapping the residuals.

Value

The output is a p by r matrix.

bootse

The standard error for elements in beta computed by bootstrap.

Examples

data(wheatprotein)
X <- wheatprotein[, 1:6]
Y <- wheatprotein[, 7]

B <- 100
bootse <- boot.xenv(X, Y, 4, B)
bootse

therimalaya/envelope documentation built on May 29, 2019, 1:38 p.m.