summary.CAISEr {CAISEr} | R Documentation |
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()
.
## S3 method for class 'CAISEr' summary(object, test = NULL, alternative = NULL, sig.level = NULL, ...)
object |
list object of class CAISEr
(generated by |
test |
type of test to be used ("t.test", "wilcoxon" or "binomial") |
alternative |
type of alternative hypothesis ("two.sided" or
"less" or "greater"). See |
sig.level |
desired family-wise significance level (alpha) for the experiment |
... |
other parameters to be passed down to specific summary functions (currently unused) |
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.
# 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")