boot.eppls: Bootstrap for eppls

In Renvlp: Computing Envelope Estimators

Bootstrap for eppls

Description

Compute bootstrap standard error for the Envelope-based Partial Partial Least Squares estimator.

Usage

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

Arguments

X1	An n by p1 matrix of continuous predictors, where p1 is the number of continuous predictors with p1 < n.
X2	An n by p2 matrix of categorical predictors, where p2 is the number of categorical predictors with p2 < n.
Y	An n by r matrix of multivariate responses, where r is the number of responses.
u	A given dimension of the Envelope-based Partial Partial Least Squares. It should be an interger between 0 and p1.
B	Number of bootstrap samples. A positive integer.

Details

This function computes the bootstrap standard errors for the regression coefficients beta1 and beta2 in the Envelope-based Partial Partial Least Squares by bootstrapping the residuals.

Value

The output is a list that contains the following components:

bootse1	The standard error for elements in beta1 computed by bootstrap. The output is an p1 by r matrix.
bootse1	The standard error for elements in beta2 computed by bootstrap. The output is an p2 by r matrix.

Examples

data(amitriptyline)
  
Y <- amitriptyline[ , 1:2]
X1 <- amitriptyline[ , 4:7]
X2 <- amitriptyline[ , 3]

B <- 100
## Not run: bootse <- boot.eppls(X1, X2, Y, 2, B)
## Not run: bootse

Renvlp documentation built on Jan. 8, 2023, 1:08 a.m.