boot.eppls | R Documentation |
Compute bootstrap standard error for the Envelope-based Partial Partial Least Squares estimator.
boot.eppls(X1, X2, Y, u, B)
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. |
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.
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. |
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
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