print.CAISEr: print.CAISEr

In fcampelo/CAISEr: Comparison of Algorithms with Iterative Sample Size Estimation

print.CAISEr

Description

S3 method for printing CAISEr objects (the output of run_experiment()).

Usage

## S3 method for class 'CAISEr'
print(x, ..., echo = TRUE, digits = 4, right = TRUE, breakrows = FALSE)

Arguments

x	list object of class CAISEr (generated by run_experiment())
...	other parameters to be passed down to specific summary functions (currently unused)
echo	logical flag: should the print method actually print to screen?
digits	the minimum number of significant digits to be used. See print.default().
right	logical, indicating whether or not strings should be right-aligned.
breakrows	logical, indicating whether to "widen" the output table by placing the bottom half to the right of the top half.

Value

data frame object containing the summary table (invisibly)

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)
my.results

fcampelo/CAISEr documentation built on Nov. 28, 2022, 3:15 a.m.