weights: Extract Summary Information Pertaining to the Bootstrapped...

In mvdalab: Multivariate Data Analysis Laboratory

Extract Summary Information Pertaining to the Bootstrapped weights

Description

Functions to extract weights bootstrap information from mvdalab objects.

Usage

## 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 ([e-mail])

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

data(Penta)
mod1 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1],
               ncomp = 2, validation = "loo")
weights(mod1, ncomp = 2, conf = .95)

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