logs_sample: Logarithmic score (sample-based version)

In epiforecasts/scoringutils: Utilities for Scoring and Assessing Predictions

Logarithmic score (sample-based version)

Description

This function is a wrapper around the logs_sample() function from the scoringRules package.

The function should be used to score continuous predictions only. While the Log Score is in theory also applicable to discrete forecasts, the problem lies in the implementation: The Log score needs a kernel density estimation, which is not well defined with integer-valued Monte Carlo Samples. The Log score can be used for specific discrete probability distributions. See the scoringRules package for more details.

Usage

logs_sample(observed, predicted, ...)

Arguments

observed	A vector with observed values of size n
predicted	nxN matrix of predictive samples, n (number of rows) being the number of data points and N (number of columns) the number of Monte Carlo samples. Alternatively, predicted can just be a vector of size n.
...	Additional arguments passed to logs_sample() from the scoringRules package.

Value

Vector with scores.

References

Alexander Jordan, Fabian Krüger, Sebastian Lerch, Evaluating Probabilistic Forecasts with scoringRules, https://www.jstatsoft.org/article/view/v090i12

Examples

observed <- rpois(30, lambda = 1:30)
predicted <- replicate(200, rpois(n = 30, lambda = 1:30))
logs_sample(observed, predicted)

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