R: Bootstrapping routine for 'mvdareg' objects

mvdaboot {mvdalab}

R Documentation

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 (nelson.afanador@mvdalab.com), Thanh Tran (thanh.tran@mvdalab.com)

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.

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

[Package mvdalab version 1.7 Index]

Bootstrapping routine for mvdareg objects