get_forecast_type: Infer forecast type from data

In epiforecasts/scoringutils: Utilities for Scoring and Assessing Predictions

Infer forecast type from data

Description

Helper function to infer the forecast type based on a data.frame or similar with forecasts and observed values. See the details section below for information on the different forecast types.

Usage

get_forecast_type(data)

Arguments

data	A data.frame (or similar) with predicted and observed values. See the details section of as_forecast() for additional information on required input formats.

Value

Character vector of length one with either "binary", "quantile", "sample" or "point".

Forecast types and input formats

Various different forecast types / forecast formats are supported. At the moment, those are:

• point forecasts

• binary forecasts ("soft binary classification")

• Probabilistic forecasts in a quantile-based format (a forecast is represented as a set of predictive quantiles)

• Probabilistic forecasts in a sample-based format (a forecast is represented as a set of predictive samples)

Forecast types are determined based on the columns present in the input data. Here is an overview of the required format for each forecast type:

All forecast types require a data.frame or similar with columns observed predicted, and model.

Point forecasts require a column observed of type numeric and a column predicted of type numeric.

Binary forecasts require a column observed of type factor with exactly two levels and a column predicted of type numeric with probabilities, corresponding to the probability that observed is equal to the second factor level. See details here for more information.

Quantile-based forecasts require a column observed of type numeric, a column predicted of type numeric, and a column quantile_level of type numeric with quantile-levels (between 0 and 1).

Sample-based forecasts require a column observed of type numeric, a column predicted of type numeric, and a column sample_id of type numeric with sample indices.

For more information see the vignettes and the example data (example_quantile, example_sample_continuous, example_sample_discrete, example_point(), and example_binary).

epiforecasts/scoringutils documentation built on April 23, 2024, 4:56 p.m.