se_boot {CAISEr}  R Documentation 
Bootstrap standard errors
Description
Calculates the standard errors of a given statistic using bootstrap
Usage
se_boot(Xk, dif = "simple", comparisons = "all.vs.all", boot.R = 999, ...)
Arguments
Xk 
list object where each position contains a vector of observations of algorithm k on a given problem instance. 
dif 
name of the difference for which the SEs are desired. Accepts "perc" (for percent differences) or "simple" (for simple differences) 
comparisons 
standard errors to be calculated. Accepts "all.vs.first"
(in which cases the first object in 
boot.R 
(optional) number of bootstrap resamples
(if 
... 
other parameters (used only for compatibility with calls to

Value
Data frame containing, for each pair of interest, the estimated difference (column "Phi") and the sample standard error (column "SE")
References
A.C. Davison, D.V. Hinkley: Bootstrap methods and their application. Cambridge University Press (1997)
F. Campelo, F. Takahashi: Sample size estimation for power and accuracy in the experimental comparison of algorithms. Journal of Heuristics 25(2):305338, 2019.
Author(s)
Felipe Campelo (fcampelo@ufmg.br, f.campelo@aston.ac.uk)
Examples
# three vectors of normally distributed observations
set.seed(1234)
Xk < list(rnorm(10, 5, 1), # mean = 5, sd = 1,
rnorm(20, 10, 2), # mean = 10, sd = 2,
rnorm(20, 15, 5)) # mean = 15, sd = 3
se_boot(Xk, dif = "simple", comparisons = "all.vs.all")
se_boot(Xk, dif = "perc", comparisons = "all.vs.first")
se_boot(Xk, dif = "perc", comparisons = "all.vs.all")