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 |

`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")
```

[Package *CAISEr* version 1.0.17 Index]