print.CAISEr {CAISEr} | R Documentation |
S3 method for printing CAISEr objects (the output of
run_experiment()
).
## S3 method for class 'CAISEr' print(x, ..., echo = TRUE, digits = 4, right = TRUE, breakrows = FALSE)
x |
list object of class CAISEr
(generated by |
... |
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 |
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. |
data frame object containing the summary table (invisibly)
# 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