bca.cis {mvdalab}

Bias-corrected and Accelerated Confidence Intervals

Description

Computes bootstrap BCa confidence intervals for chosen parameters for PLS models fitted with validation = "oob".

Usage

bca.cis(object, conf = .95, type = c("coefficients",
        "loadings", "weights"))

Arguments

object

an object of class "mvdareg", i.e. plsFit.

conf

desired confidence level

type

input parameter vector

Details

The function computes the bootstrap BCa confidence intervals for any fitted mvdareg model. Should be used in instances in which there is reason to suspectd the percentile intervals. Results provided across all latent variables (LVs). As such, it may be slow for models with a large number of LVs.

Value

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

ncomp

number of components in the model

variables

variable names

boot.mean

mean of the bootstrap

BCa percentiles

confidence intervals

proportional bias

calculated bias

skewness

skewness of the bootstrap distribution

a

acceleration contstant

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.

See Also

plsFit, mvdaboot, boot.plots

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)
bca.cis(mod1, conf = .95, type = "coefficients")
## Not run: 
bca.cis(mod1, conf = .95, type = "loadings")
bca.cis(mod1, conf = .95, type = "weights")

## End(Not run)