pit: Probability Integral Transformation (data.frame Format)

In scoringutils: Utilities for Scoring and Assessing Predictions

Probability Integral Transformation (data.frame Format)

Description

Wrapper around pit() for use in data.frames

Usage

pit(data, by, n_replicates = 100)

Arguments

data	a data.frame with the following columns: true_value, prediction, sample.
by	Character vector with the columns according to which the PIT values shall be grouped. If you e.g. have the columns 'model' and 'location' in the data and want to have a PIT histogram for every model and location, specify by = c("model", "location").
n_replicates	the number of draws for the randomised PIT for integer predictions.

Details

see pit()

Value

a data.table with PIT values according to the grouping specified in by

References

Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15, doi: 10.1371/journal.pcbi.1006785

Examples

result <- pit(example_continuous, by = "model")
plot_pit(result)

# example with quantile data
result <- pit(example_quantile, by = "model")
plot_pit(result)

scoringutils documentation built on Feb. 16, 2023, 7:30 p.m.