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 -0.76278812 -3.51748249 0.7966103
#> 2 100 0.85397764 0.28478387 3.3023528
#> 3 100 0.51680901 -0.99036841 2.7685163
#> 4 100 1.09401737 0.57896491 4.2609446
#> 5 100 0.73304685 -0.40343203 3.5030764
#> 6 100 -0.01193333 -1.83911214 2.2211043
#> 7 100 0.06527588 -1.92976878 1.9234330
#> 8 100 -0.82803558 -3.58631744 0.1801371
#> 9 100 0.87056501 -0.01212261 3.5892404
#> 10 100 -0.23716897 -2.50485763 1.3559333
if(adegraphicsLoaded())
plot(bt)