confidence: Bootstrap confidence intervals

In resample: Resampling Functions

Bootstrap confidence intervals

Description

Bootstrap confidence intervals - percentile method or t interval.

Usage

CI.percentile(x, confidence = 0.95, expand = TRUE, ...,
              probs = sort(1 + c(-1, 1) * confidence) / 2)
CI.t(x, confidence = 0.95, expand = TRUE,
              probs = sort(1 + c(-1, 1) * confidence) / 2)
CI.bca(x, confidence = 0.95,
              expand = TRUE, L = NULL,
              probs = sort(1 + c(-1, 1) * confidence) / 2)
CI.bootstrapT(x, confidence = 0.95,
              probs = sort(1 + c(-1, 1) * confidence) / 2)

Arguments

x	a bootstrap or bootstrap object.
confidence	confidence level, between 0 and 1. The default 0.95 gives a 95% two-sided interval.
expand	logical, if TRUE then use modified percentiles for better small-sample accuracy.
...	additional arguments to pass to quantile.resample and quantile.
probs	probability values, between 0 and 1. confidence = 0.95 corresponds to probs = c(0.025, 0.975). If this is supplied then confidence is ignored.
L	vector of length n, empirical influence function values. If not supplied this is computed using jackknife.

Details

CI.bootstrapT assumes the first dimension of the statistic is an estimate, and the second is proportional to a SE for the estimate. E.g. for bootstrapping the mean, they could be the mean and s. This is subject to change.

CI.bca and CI.bootstrapT currently only support a single sample.

Value

a matrix with one column for each value in probs and one row for each statistic.

Author(s)

Tim Hesterberg [e-mail],
https://www.timhesterberg.net/bootstrap-and-resampling

References

This discusses the expanded percentile interval: Hesterberg, Tim (2014), What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum, https://arxiv.org/abs/1411.5279.

See Also

bootstrap, bootstrap2, ExpandProbs (for the expanded intervals).

Examples

# See full set of examples in resample-package, including different
# ways to call all four functions depending on the structure of the data.
data(Verizon)
CLEC <- with(Verizon, Time[Group == "CLEC"])
bootC <- bootstrap(CLEC, mean, seed = 0)
bootC2 <- bootstrap(CLEC, c(mean = mean(CLEC), sd = sd(CLEC)), seed = 0)
CI.percentile(bootC)
CI.t(bootC)
CI.bca(bootC)
CI.bootstrapT(bootC2)

resample documentation built on June 13, 2022, 5:08 p.m.