Extract Summary Information Pertaining to the Bootstrapped weights

Description

Functions to extract weights bootstrap information from mvdalab objects.

Usage

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## S3 method for class 'mvdareg'
weights(object, ncomp = object$ncomp, conf = .95, ...)

Arguments

object

an mvdareg or mvdapaca object, i.e. plsFit.

ncomp

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

conf

for a bootstrapped model, the confidence level to use.

...

additional arguments. Currently ignored.

Details

weights is used to extract a summary of the weights of a PLS. If ncomps is missing (or is NULL), summaries for all regression estimates are returned. Otherwise, if comps is given parameters for a model with only the requested component comps is returned.

For mvdareg objects only, boostrap summaries provided are for actual regression weights, bootstrap percentiles, bootstrap mean, skewness, and bias. These summaries can also be extracted using weight.boots

Value

A weights object contains a data frame with columns:

variable

variable names.

Actual

Actual loading estimate using all the data.

BCa percentiles

confidence intervals.

boot.mean

mean of the bootstrap.

skewness

skewness of the bootstrap distribution.

bias

estimate of bias w.r.t. the loading estimate.

Bootstrap Error

estimate of bootstrap standard error.

t value

approximate 't-value' based on the Bootstrap Error.

bias t value

approximate 'bias t-value' based on the Bootstrap Error.

Author(s)

Nelson Lee Afanador (nelson.afanador@mvdalab.com)

References

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

Efron, B. (1992) Jackknife-after-bootstrap standard errors and influence functions (with Discussion). Journal of the Royal Statistical Society, B, 54, 83:127.

See Also

weightsplot, weight.boots, weightsplot2D

Examples

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data(Penta)
## Number of bootstraps set to 500 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 = 500)
weights(mod1, ncomp = 2, conf = .95)

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