bootstrap | R Documentation |
Samples randomly from the elements of object
with replacement.
bootstrap(object, ...)
## S4 method for signature 'numeric'
bootstrap(
object,
do,
n,
...,
f = NULL,
level = 0.95,
interval = c("basic", "normal", "percentiles")
)
object |
A |
... |
Extra arguments to be passed to |
do |
A |
n |
A non-negative |
f |
A |
level |
A length-one |
interval |
A |
If f
is NULL
(the default), bootstrap()
returns a named numeric
vector with the following elements:
original
The observed value of do
applied to object
.
mean
The bootstrap estimate of mean of do
.
bias
The bootstrap estimate of bias of do
.
error
The bootstrap estimate of standard error of do
.
lower
The lower limit of the bootstrap confidence interval at level
.
upper
The upper limit of the bootstrap confidence interval at level
If f
is a function
, bootstrap()
returns the result of f
applied to
the n
values of do
.
N. Frerebeau
Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge Series on Statistical and Probabilistic Mathematics. Cambridge: Cambridge University Press.
confidence_bootstrap()
Other resampling methods:
jackknife()
,
resample_multinomial()
,
resample_uniform()
x <- rnorm(20)
## Bootstrap
bootstrap(x, do = mean, n = 100)
## Estimate the 25th and 95th percentiles
quant <- function(x) { quantile(x, probs = c(0.25, 0.75)) }
bootstrap(x, n = 100, do = mean, f = quant)
## Get the n bootstrap estimates
(z <- bootstrap(x, n = 100, do = mean, f = function(x) { x }))
## Basic bootstrap confidence interval
confidence_bootstrap(z, level = 0.95, type = "basic", t0 = mean(x))
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