perc.cis: Percentile Bootstrap Confidence Intervals
In mvdalab: Multivariate Data Analysis Laboratory

perc.cis

R Documentation

Percentile Bootstrap Confidence Intervals

Description

Computes percentile bootstrap confidence intervals for chosen parameters for plsFit models fitted with validation = "oob"

Usage

perc.cis(object, ncomp = object$ncomp, conf = 0.95, 
        type = c("coefficients", "loadings", "weights"))

Arguments

`object`	an object of class `"mvdareg"`, i.e., `plsFit`
`ncomp`	number of components to extract percentile intervals.
`conf`	confidence level.
`type`	input parameter vector.

Details

The function fits computes the bootstrap percentile confidence intervals for any fitted mvdareg model.

Value

A perc.cis object contains component results for the following:

`ncomp`	number of components in the model
`variables`	variable names
`boot.mean`	mean of the bootstrap
`percentiles`	confidence intervals

Author(s)

Nelson Lee Afanador (nelson.afanador@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.

Examples

data(Penta)
## Number of bootstraps set to 250 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 = 250)
perc.cis(mod1, ncomp = 1:2, conf = .95, type = "coefficients")

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