perc.cis {mvdalab}

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 ([e-mail])

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")