scoringutils (version 0.1.7.2)

sharpness: Determines sharpness of a probabilistic forecast

Description

Determines sharpness of a probabilistic forecast

Usage

sharpness(predictions)

Arguments

predictions

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

Value

vector with sharpness values

Details

Sharpness is the ability of the model to generate predictions within a narrow range. It is a data-independent measure, and is purely a feature of the forecasts themselves.

Shaprness of predictive samples corresponding to one single true value is measured as the normalised median of the absolute deviation from the median of the predictive samples. For details, see mad

References

Funk S, Camacho A, Kucharski AJ, Lowe R, Eggo RM, Edmunds WJ (2019) Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15. PLoS Comput Biol 15(2): e1006785. <doi:10.1371/journal.pcbi.1006785>

Examples

# NOT RUN {
predictions <- replicate(200, rpois(n = 30, lambda = 1:30))
sharpness(predictions)
# }