coefficients.boots: BCa Summaries for the coefficient of an mvdareg object

In mvdalab: Multivariate Data Analysis Laboratory

BCa Summaries for the coefficient of an mvdareg object

Description

Computes bootstrap BCa confidence intervals for regression coefficients, along with expanded bootstrap summaries.

Usage

coefficients.boots(object, ncomp = object$ncomp, conf = 0.95)

Arguments

object	an object of class mvdareg, i.e., a plsFit.
ncomp	number of components in the model
conf	desired confidence level

Details

The function computes the bootstrap BCa confidence intervals for fitted mvdareg models where valiation = "oob". Should be used in instances in which there is reason to suspectd the percentile intervals. Results provided across all latent variables or for specific latent variables via ncomp.

Value

A coefficients.boots object contains component results for the following:

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

Author(s)

Nelson Lee Afanador ([e-mail])

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. 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

coef, coefficients, coefsplot, coefficients

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)
coefficients.boots(mod1, ncomp = 2, conf = .95)

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