# boot.eppls: Bootstrap for eppls In Renvlp: Computing Envelope Estimators

 boot.eppls R Documentation

## 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 Oct. 11, 2023, 1:06 a.m.