loadings: Summary Information Pertaining to the Bootstrapped Loadings

In mvdalab: Multivariate Data Analysis Laboratory

Summary Information Pertaining to the Bootstrapped Loadings

Description

Functions to extract loadings bootstrap information from mvdalab objects.

Usage

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

Arguments

object	an mvdareg or mvdapaca object. A fitted model.
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

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

Boostrap summaries are provided for mvdareg objects where validation = "oob". These summaries can also be extracted using loadings.boots

Value

A loadings 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

There are many references explaining the bootstrap. Among them are:

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

loadingsplot, loadings.boots, loadingsplot2D

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)
loadings(mod1, ncomp = 2, conf = .95)

data(iris)
pc1 <- pcaFit(iris)
loadings(pc1)

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