mvdalab (version 1.7)

loadings: Summary Information Pertaining to the Bootstrapped Loadings

Description

Functions to extract loadings bootstrap information from mvdalab objects.

Usage

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

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

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.

Author

Nelson Lee Afanador ()

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

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)