scoringutils (version 0.1.7.2)

plot_pairwise_comparison: Plot Heatmap of Pairwise Comparisons

Description

Creates a heatmap of the ratios or pvalues from a pairwise comparison between models

Usage

plot_pairwise_comparison(
  comparison_result,
  type = c("mean_scores_ratio", "pval", "together"),
  smaller_is_good = TRUE,
  facet_formula = NULL,
  scales = "free_y",
  ncol = NULL,
  facet_wrap_or_grid = "facet_wrap"
)

Arguments

comparison_result

A data.frame as produced by pairwise_comparison

type

character vector of length one that is either "mean_scores_ratio" or "pval". This denotes whether to visualise the ratio or the p-value of the pairwise comparison. Default is "mean_scores_ratio"

smaller_is_good

logical (default is TRUE) that indicates whether smaller or larger values are to be interpreted as 'good' (as you could just invert the mean scores ratio)

facet_formula

facetting formula passed down to ggplot. Default is NULL

scales

scales argument that gets passed down to ggplot. Only necessary if you make use of facetting. Default is "free_y"

ncol

Number of columns for facet wrap. Only relevant if facet_formula is given and facet_wrap_or_grid == "facet_wrap"

facet_wrap_or_grid

Use ggplot2's facet_wrap or facet_grid? Anything other than "facet_wrap" will be interpreted as facet_grid. This only takes effect if facet_formula is not NULL

Examples

# NOT RUN {
df <- data.frame(model = rep(c("model1", "model2", "model3"), each = 10),
                 id = rep(1:10),
                 interval_score = abs(rnorm(30, mean = rep(c(1, 1.3, 2), each = 10))),
                 aem = (abs(rnorm(30))))

data <- scoringutils::quantile_example_data
scores <- scoringutils::eval_forecasts(data)
pairwise <- pairwise_comparison(scores,
                                summarise_by = "value_desc")
scoringutils::plot_pairwise_comparison(pairwise,
                                       facet_formula = ~ value_desc,
                                       scales = "fixed")
# }