perc.cis: Percentile Bootstrap Confidence Intervals In mvdalab: Multivariate Data Analysis Laboratory

Description

Computes percentile bootstrap confidence intervals for chosen parameters for `plsFit` models fitted with `validation = "oob"`

Usage

 ```1 2``` ```perc.cis(object, ncomp = object\$ncomp, conf = 0.95, type = c("coefficients", "loadings", "weights")) ```

Arguments

 `object` an object of class `"mvdareg"`, i.e., `plsFit` `ncomp` number of components to extract percentile intervals. `conf` confidence level. `type` input parameter vector.

Details

The function fits computes the bootstrap percentile confidence intervals for any fitted `mvdareg` model.

Value

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

 `ncomp` number of components in the model `variables` variable names `boot.mean` mean of the bootstrap `percentiles` confidence intervals

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.

Examples

 ```1 2 3 4 5 6``` ```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) perc.cis(mod1, ncomp = 1:2, conf = .95, type = "coefficients") ```

mvdalab documentation built on Nov. 17, 2017, 6 a.m.