mvdalab (version 1.7)

mvdaboot: Bootstrapping routine for mvdareg objects

Description

When validation = 'oob' this routine effects the bootstrap procedure for mvdareg objects.

Usage

mvdaboot(X, Y, ncomp, method = "bidiagpls", scale = FALSE, n_cores, parallel,
          boots, ...)

Value

Provides the following bootstrapped results as a list for mvdareg objects:

coefficients

fitted values

weights

weights

loadings

loadings

ncomp

number of latent variables

bootstraps

No. of bootstraps

scores

scores

cvR2

bootstrap estimate of cvR2

PRESS

bootstrap estimate of prediction error sums of squares

MSPRESS

bootstrap estimate of mean squared error prediction sums of squares

boot.means

bootstrap mean of bootstrapped parameters

RMSPRESS

bootstrap estimate of mean squared error prediction sums of squares

D2

bidiag2 matrix

iD2

Inverse of bidiag2 matrix

y.loadings

normalized y-loadings

y.loadings2

non-normalized y-loadings

MSPRESS.632

.632 corrected estimate of MSPRESS

oob.fitted

out-of-bag PLS fitted values

RMSPRESS.632

.632 corrected estimate of RMSPRESS

in.bag

bootstrap samples used for model building at each bootstrap

Arguments

X

a matrix of observations. NAs and Infs are not allowed.

Y

a vector. NAs and Infs are not allowed.

ncomp

the number of components to include in the model (see below).

method

PLS algorithm used.

scale

scaling used.

n_cores

No. of cores to run for parallel processing. Currently set to 2 (4 max).

parallel

should parallelization be used.

boots

No. of bootstrap samples when validation = 'oob'

...

additional arguments. Currently ignored.

Author

Nelson Lee Afanador (), Thanh Tran ()

Details

This function should not be called directly, but through the generic function plsFit with the argument validation = 'oob'.

References

There are many references explaining the bootstrap and its implementation for confidence interval estimation. Among them are:

Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.

Efron, B. and Tibshirani, R. (1993) An Introduction to the Bootstrap. Chapman & Hall.

Hinkley, D.V. (1988) Bootstrap methods (with Discussion). Journal of the Royal Statistical Society, B, 50, 312:337, 355:370.

NOTE: This function is adapted from mvr in package pls with extensive modifications by Nelson Lee Afanador and Thanh Tran.

See Also

plsFit, mvdaloo

Examples

data(Penta)
## Number of bootstraps set to 300 to demonstrate flexibility
## Use a minimum of 1000 (default) for results that support bootstraping
mod1 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1], 
               ncomp = 2, validation = "oob", boots = 300)

## Run line below to see bootstrap results
## mod1$validation