CI.percentile
From resample v0.4
by Tim Hesterberg
Bootstrap confidence intervals
Bootstrap confidence intervals  percentile method or t interval.
 Keywords
 htest, nonparametric
Usage
CI.percentile(x, probs = c(0.025, 0.975), expand = TRUE, ...)
CI.t(x, probs = c(0.025, 0.975))
CI.bca(x, probs = c(0.025, 0.975), expand = TRUE, L = NULL, ...)
CI.bootstrapT(x, probs = c(0.025, 0.975))
Arguments
 x

a
bootstrap
orbootstrap
object.  probs

probability values, between 0 and 1. The default vector
c(0.025, 0.975)
gives a 95% twosided interval.  expand

logical, if
TRUE
then use modified percentiles for better smallsample accuracy.  ...

additional arguments to pass to
quantile.resample
andquantile
.  L
 vector of length
n
, empirical influence function values. If not supplied this is computed usingjackknife
.
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.
References
This discusses the expanded percentile interval: Hesterberg, Tim (2014), What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum, http://arxiv.org/abs/1411.5279.
See Also
bootstrap
,
bootstrap2
,
ExpandProbs
(for the expanded intervals).
Examples
## Not run:
# # See full set of examples in resamplepackage, 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)
# ## End(Not run)
Community examples
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