Bootstrap simulations
randboot.RdFunctions and classes to manage outputs of bootstrap
simulations for one (class randboot) or several (class krandboot) statistics
Usage
as.krandboot(obs, boot, quantiles = c(0.025, 0.975), names =
colnames(boot), call = match.call())
# S3 method for class 'krandboot'
print(x, ...)
as.randboot(obs, boot, quantiles = c(0.025, 0.975), call = match.call())
# S3 method for class 'randboot'
print(x, ...)
randboot(object, ...)Arguments
- obs
a value (class
randboot) or a vector (classkrandboot) with observed statistics- boot
a vector (class
randboot) or a matrix (classkrandboot) with the bootstrap values of the statistics- quantiles
a vector indicating the lower and upper quantiles to compute
- names
a vector of names for the statistics
- call
the matching call
- x
an object of class
randbootorkrandboot- object
an object on which bootstrap should be perform
- ...
other arguments to be passed to methods
References
Carpenter, J. and Bithell, J. (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians.Statistics in medicine, 19, 1141-1164
Author
Stéphane Dray (stephane.dray@univ-lyon1.fr)
Examples
## an example corresponding to 10 statistics and 100 repetitions
bt <- as.krandboot(obs = rnorm(10), boot = matrix(rnorm(1000), nrow = 100))
bt
#> Multiple bootstrap
#> Call: as.krandboot(obs = rnorm(10), boot = matrix(rnorm(1000), nrow = 100))
#>
#> Number of statistics: 10
#>
#> Confidence Interval:
#> N.rep Obs 2.5% 97.5%
#> 1 100 1.21408203 0.7715575 4.67941414
#> 2 100 0.06300424 -2.0151553 1.67623176
#> 3 100 0.63448332 -0.8950366 3.04754181
#> 4 100 -0.09939149 -1.9969772 1.34908788
#> 5 100 0.79182714 -0.3287536 3.22141009
#> 6 100 -0.96810189 -4.4294455 0.20871860
#> 7 100 -0.55865763 -2.9482890 0.02828037
#> 8 100 1.42610669 1.1714089 4.65312399
#> 9 100 -1.09529548 -3.9888019 -0.19504235
#> 10 100 0.07309591 -2.0980403 1.83286116
if(adegraphicsLoaded())
plot(bt)