mvdaboot: Bootstrapping routine for 'mvdareg' objects

In mvdalab: Multivariate Data Analysis Laboratory

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, ...)

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.

Details

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

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

Author(s)

Nelson Lee Afanador ([e-mail]), Thanh Tran ([e-mail])

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

mvdalab documentation built on Oct. 6, 2022, 1:05 a.m.