boot_sdm {CAISEr} | R Documentation |
Bootstraps the sampling distribution of the means for a given vector of observations
boot_sdm(x, boot.R = 999, ncpus = 1, seed = NULL)
x |
vector of observations |
boot.R |
number of bootstrap resamples |
ncpus |
number of cores to use |
seed |
seed for the PRNG |
vector of bootstrap estimates of the sample mean
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):305-338, 2019.
Felipe Campelo (fcampelo@ufmg.br, f.campelo@aston.ac.uk)
x <- rnorm(15, mean = 4, sd = 1) my.sdm <- boot_sdm(x) hist(my.sdm, breaks = 30) qqnorm(my.sdm, pch = 20) x <- runif(12) my.sdm <- boot_sdm(x) qqnorm(my.sdm, pch = 20) # Convergence of the SDM to a Normal distribution as sample size is increased X <- rchisq(1000, df = 3) x1 <- rchisq(10, df = 3) x2 <- rchisq(20, df = 3) x3 <- rchisq(40, df = 3) par(mfrow = c(2, 2)) plot(density(X), main = "Estimated pop distribution"); hist(boot_sdm(x1), breaks = 25, main = "SDM, n = 10") hist(boot_sdm(x2), breaks = 25, main = "SDM, n = 20") hist(boot_sdm(x3), breaks = 25, main = "SDM, n = 40") par(mfrow = c(1, 1))