BCa Summaries for the weights of an mvdareg object

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Description

Computes weights bootstrap BCa confidence intervals, along with expanded bootstrap summaries.

Usage

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weight.boots(object, ncomp = object$ncomp, conf = .95)

Arguments

object

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

ncomp

number of components in the model.

conf

desired confidence level.

Details

The function fits 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 weight.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 (nelson.afanador@mvdalab.com)

References

Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.

Efron, B. (1992) Jackknife-after-bootstrap standard errors and influence functions (with Discussion). Journal of the Royal Statistical Society, B, 54, 83:127.

Examples

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data(Penta)
## Number of bootstraps set to 500 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 = 500)
weight.boots(mod1, ncomp = 2, conf = .95)

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