bca.cis: Bias-corrected and Accelerated Confidence Intervals

In mvdalab: Multivariate Data Analysis Laboratory

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)

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