boot.penv: Bootstrap for penv

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

Description

Compute bootstrap standard error for the partial envelope estimator.

Usage

boot.penv(X1, X2, Y, u, B)

Arguments

X1	Predictors of main interest. An n by p1 matrix, n is the number of observations, and p1 is the number of main predictors. The predictors can be univariate or multivariate, discrete or continuous.
X2	Covariates, or predictors not of main interest. An n by p2 matrix, p2 is the number of covariates.
Y	Multivariate responses. An n by r matrix, r is the number of responses and n is number of observations. The responses must be continuous variables.
u	Dimension of the partial envelope. An integer between 0 and r.
B	Number of bootstrap samples. A positive integer.

Details

This function computes the bootstrap standard errors for the regression coefficients beta1 in the partial envelope model by bootstrapping the residuals.

Value

The output is an r by p1 matrix.

bootse

The standard error for elements in beta1 computed by bootstrap.

Examples

data(fiberpaper)
X1 <- fiberpaper[, 7]
X2 <- fiberpaper[, 5:6]
Y <- fiberpaper[, 1:4]

B <- 100
bootse <- boot.penv(X1, X2, Y, 1, B)
bootse

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