summary.CAISEr {CAISEr}

summary.CAISEr

Description

S3 method for summarizing CAISEr objects output by run_experiment()). Input parameters test, alternative and sig.level can be used to override the ones used in the call to run_experiment().

Usage

## S3 method for class 'CAISEr'
summary(object, test = NULL, alternative = NULL, sig.level = NULL, ...)

Arguments

object

list object of class CAISEr (generated by run_experiment())

test

type of test to be used ("t.test", "wilcoxon" or "binomial")

alternative

type of alternative hypothesis ("two.sided" or "less" or "greater"). See calc_instances() for details.

sig.level

desired family-wise significance level (alpha) for the experiment

...

other parameters to be passed down to specific summary functions (currently unused)

Value

A list object is returned invisibly, containing the details of all tests performed as well as information on the total number of runs dedicated to each algorithm.

Examples

# Example using four dummy algorithms and 100 dummy instances.
# See [dummyalgo()] and [dummyinstance()] for details.
# Generating 4 dummy algorithms here, with means 15, 10, 30, 15 and standard
# deviations 2, 4, 6, 8.
algorithms <- mapply(FUN = function(i, m, s){
  list(FUN   = "dummyalgo",
       alias = paste0("algo", i),
       distribution.fun  = "rnorm",
       distribution.pars = list(mean = m, sd = s))},
  i = c(alg1 = 1, alg2 = 2, alg3 = 3, alg4 = 4),
  m = c(15, 10, 30, 15),
  s = c(2, 4, 6, 8),
  SIMPLIFY = FALSE)

# Generate 100 dummy instances with centered exponential distributions
instances <- lapply(1:100,
                    function(i) {rate <- runif(1, 1, 10)
                                 list(FUN   = "dummyinstance",
                                      alias = paste0("Inst.", i),
                                      distr = "rexp", rate = rate,
                                      bias  = -1 / rate)})

my.results <- run_experiment(instances, algorithms,
                             d = 1, se.max = .1,
                             power = .9, sig.level = .05,
                             power.target = "mean",
                             dif = "perc", comparisons = "all.vs.all",
                             seed = 1234, ncpus = 1)
summary(my.results)

# You can override some defaults if you want:
summary(my.results, test = "wilcoxon")