coefficients.boots {mvdalab}

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)