get_metrics.forecast_sample: Get default metrics for sample-based forecasts

In epiforecasts/scoringutils: Utilities for Scoring and Assessing Predictions

Get default metrics for sample-based forecasts

Description

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

• "crps" = crps_sample()

• "overprediction" = overprediction_sample()

• "underprediction" = underprediction_sample()

• "dispersion" = dispersion_sample()

• "log_score" = logs_sample()

• "dss" = dss_sample()

• "mad" = mad_sample()

• "bias" = bias_sample()

• "ae_median" = ae_median_sample()

• "se_mean" = se_mean_sample()

Usage

## S3 method for class 'forecast_sample'
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

Overview of required input format for sample-based forecasts

See Also

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

Examples

get_metrics(example_sample_continuous, exclude = "mad")

epiforecasts/scoringutils documentation built on Dec. 11, 2024, 11:12 a.m.