This function does the pairwise comparison for one set of forecasts, but
multiple models involved. It gets called from get_pairwise_comparisons().
get_pairwise_comparisons() splits the data into arbitrary subgroups
specified by the user (e.g. if pairwise comparison should be done separately
for different forecast targets) and then the actual pairwise comparison for
that subgroup is managed from pairwise_comparison_one_group(). In order to
actually do the comparison between two models over a subset of common
forecasts it calls compare_forecasts().
pairwise_comparison_one_group(
scores,
metric,
baseline,
compare = "model",
by,
...
)A data.table with the results of pairwise comparisons
containing the mean score ratios (mean_scores_ratio),
unadjusted (pval) and adjusted (adj_pval) p-values, and relative skill
values of each model (..._relative_skill). If a baseline model is given
then the scaled relative skill is reported as well
(..._scaled_relative_skill).
An object of class scores (a data.table with
scores and an additional attribute metrics as produced by score()).
A string with the name of the metric for which a relative skill shall be computed. By default this is either "crps", "wis" or "brier_score" if any of these are available.
A string with the name of a model. If a baseline is
given, then a scaled relative skill with respect to the baseline will be
returned. By default (NULL), relative skill will not be scaled with
respect to a baseline model.
Character vector with a single colum name that defines the elements for the pairwise comparison. For example, if this is set to "model" (the default), then elements of the "model" column will be compared.
Character vector with column names that define further grouping
levels for the pairwise comparisons. By default this is NULL and there
will be one relative skill score per distinct entry of the column selected
in compare. If further columns are given here, for example, by = "location" with compare = "model", then one separate relative skill
score is calculated for every model in every location.
Additional arguments for the comparison between two models. See
compare_forecasts() for more information.