Get default metrics for quantile-based forecasts

For quantile-based forecasts, the default scoring rules are:

• "wis" = wis()

• "overprediction" = overprediction_quantile()

• "underprediction" = underprediction_quantile()

• "dispersion" = dispersion_quantile()

• "bias" = bias_quantile()

• "interval_coverage_50" = interval_coverage()

• "interval_coverage_90" = purrr::partial( interval_coverage, interval_range = 90 )

• "ae_median" = ae_median_quantile()

Note: The interval_coverage_90 scoring rule is created by modifying interval_coverage(), making use of the function purrr::partial(). This construct allows the function to deal with arbitrary arguments in ..., while making sure that only those that interval_coverage() can accept get passed on to it. interval_range = 90 is set in the function definition, as passing an argument interval_range = 90 to score() would mean it would also get passed to interval_coverage_50.

Usage

# S3 method for class 'forecast_quantile'
get_metrics(x, select = NULL, exclude = NULL, ...)

Arguments

x

A forecast object (a validated data.table with predicted and observed values, see as_forecast_binary()).

select

A character vector of scoring rules to select from the list. If select is NULL (the default), all possible scoring rules are returned.

exclude

A character vector of scoring rules to exclude from the list. If select is not NULL, this argument is ignored.

...

unused

Input format

See also

Other get_metrics functions: get_metrics(), get_metrics.forecast_binary(), get_metrics.forecast_nominal(), get_metrics.forecast_point(), get_metrics.forecast_sample(), get_metrics.scores()

Examples

get_metrics(example_quantile, select = "wis")
#> $wis
#> function (observed, predicted, quantile_level, separate_results = FALSE, 
#>     weigh = TRUE, count_median_twice = FALSE, na.rm = FALSE) 
#> {
#>     assert_input_quantile(observed, predicted, quantile_level)
#>     reformatted <- quantile_to_interval(observed, predicted, 
#>         quantile_level)
#>     interval_ranges <- get_range_from_quantile(quantile_level[quantile_level != 
#>         0.5])
#>     complete_intervals <- duplicated(interval_ranges) | duplicated(interval_ranges, 
#>         fromLast = TRUE)
#>     if (!all(complete_intervals) && !isTRUE(na.rm)) {
#>         incomplete <- quantile_level[quantile_level != 0.5][!complete_intervals]
#>         cli_abort(c(`!` = "Not all quantile levels specified form symmetric prediction\n        intervals.\n        The following quantile levels miss a corresponding lower/upper bound:\n        {.val {incomplete}}.\n        You can drop incomplete prediction intervals using `na.rm = TRUE`."))
#>     }
#>     assert_logical(separate_results, len = 1)
#>     assert_logical(weigh, len = 1)
#>     assert_logical(count_median_twice, len = 1)
#>     assert_logical(na.rm, len = 1)
#>     if (separate_results) {
#>         cols <- c("wis", "dispersion", "underprediction", "overprediction")
#>     }
#>     else {
#>         cols <- "wis"
#>     }
#>     reformatted[, `:=`(eval(cols), do.call(interval_score, list(observed = observed, 
#>         lower = lower, upper = upper, interval_range = interval_range, 
#>         weigh = weigh, separate_results = separate_results)))]
#>     if (count_median_twice) {
#>         reformatted[, `:=`(weight, 1)]
#>     }
#>     else {
#>         reformatted[, `:=`(weight, ifelse(interval_range == 0, 
#>             0.5, 1))]
#>     }
#>     reformatted <- reformatted[, lapply(.SD, weighted.mean, na.rm = na.rm, 
#>         w = weight), by = "forecast_id", .SDcols = colnames(reformatted) %like% 
#>         paste(cols, collapse = "|")]
#>     if (separate_results) {
#>         return(list(wis = reformatted$wis, dispersion = reformatted$dispersion, 
#>             underprediction = reformatted$underprediction, overprediction = reformatted$overprediction))
#>     }
#>     else {
#>         return(reformatted$wis)
#>     }
#> }
#> <bytecode: 0x564167b0d5a0>
#> <environment: namespace:scoringutils>
#>