Usage
SRCSranks(data, params, target, performance, test = c("wilcoxon", "t",
"tukeyHSD", "custom"), fun = NULL, correction = p.adjust.methods,
alpha = 0.05, maximize = TRUE, ncores = 1, paired = FALSE, ...)Arguments
data
A dataframe object containing (at least) two columns for the target factor and the performance measure
Additional columns are aimed at grouping the problem configuration by (at most) 3 different factors.
params
A vector with the column names in data that define a problem configuration. If not already factor objects, those columns will be converted to
factors inside the function (note this does not alter the ordering of the levels in case it was
target
Name of the target column of data. For each combination of the values of params, the ranks are obtained by
comparing the repeated measurements of performance associated to each level of the target colum
performance
Name of the column of data containing the repeated performance measurements. If given a vector of strings,
then a separate ranking will be computed for each of the elements, and no p-values, mean or stdev columns will be returned, just the r
test
The statistical test to be performed to compare the performance of every level of the target variable at each problem configuration.
fun
Function performing a custom statistical test, if test = "custom"; otherwise, this argument is ignored. The function must receive exactly
two vectors (the first is a vector of real numbers and the second is a factor with the level to whic
correction
The p-value adjust method. Must be one of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none" (defaults to "holm").
This parameter will be ignored if test = "tukeyHSD" as Tukey HSD incorporates its own correction procedure.
alpha
Significance threshold for pairwise comparisons. Defaults to 0.05.
maximize
Boolean indicating whether the higher the performance measure, the better (default), or vice-versa.
ncores
Number of physical CPUs available for computations. If ncores > 1, parallelization is achieved through the parallel package and
is applied to the computation of ranks for more than one proble paired
Boolean indicating whether samples in the same problem configuration, which only differ in the target value, and in the same relative position (row) within their
respective target values are paired or not. Defaults to FALSE. This should be set to TRUE, f
...
Further arguments to be passed to the function fun that is called for every pairwise comparison.