calc_se: Calculates the standard error for simple and percent...

In CAISEr: Comparison of Algorithms with Iterative Sample Size Estimation

Calculates the standard error for simple and percent differences

Description

Calculates the sample standard error for the estimator differences between multiple algorithms on a given instance.

Usage

calc_se(
  Xk,
  dif = "simple",
  comparisons = "all.vs.all",
  method = "param",
  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 algorithms is considered to be the reference algorithm) or "all.vs.all" (if there is no reference and all pairwise SEs are desired).
method	method used to calculate the interval. Accepts "param" (using parametric formulas based on normality of the sampling distribution of the means) or "boot" (for bootstrap).
boot.R	(optional) number of bootstrap resamples (if method == "boot")

Details

• If dif == "perc" it returns the standard errors for the sample estimates of pairs (mu2 - mu1) / mu, where mu1, mu2 are the means of the populations that generated sample vectors x1, x2, and

• If dif == "simple" it returns the SE for sample estimator of (mu2 - mu1)

Value

a list object containing the following items:

• Phi.est - estimated values of the statistic of interest for each pair of algorithms of interest (all pairs if comparisons == "all.vs.all", or all pairs containing the first algorithm if comparisons == "all.vs.first").

• se - standard error estimates

References

• 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.

Author(s)

Felipe Campelo ([e-mail], [e-mail])

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(50, 15, 5)) # mean = 15, sd = 3

calc_se(Xk, dif = "simple", comparisons = "all.vs.all", method = "param")
calc_se(Xk, dif = "simple", comparisons = "all.vs.all", method = "boot")

calc_se(Xk, dif = "perc", comparisons = "all.vs.first", method = "param")
calc_se(Xk, dif = "perc", comparisons = "all.vs.first", method = "boot")

calc_se(Xk, dif = "perc", comparisons = "all.vs.all", method = "param")
calc_se(Xk, dif = "perc", comparisons = "all.vs.all", method = "boot")

CAISEr documentation built on Nov. 17, 2022, 1:07 a.m.