| logs_sample {scoringutils} | R Documentation |
Logarithmic score
Description
Wrapper around the logs_sample()
function from the
scoringRules package. Used to score continuous predictions.
While the Log Score is in theory also applicable
to integer 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
integer valued probability distributions. See the scoringRules package for
more details.
Usage
logs_sample(true_values, predictions)
Arguments
true_values |
A vector with the true observed values of size n |
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. Alternatively, predictions can just be a vector of size n. |
Value
vector with the scoring values
References
Alexander Jordan, Fabian Krüger, Sebastian Lerch, Evaluating Probabilistic Forecasts with scoringRules, https://www.jstatsoft.org/article/view/v090i12
Examples
true_values <- rpois(30, lambda = 1:30)
predictions <- replicate(200, rpois(n = 30, lambda = 1:30))
logs_sample(true_values, predictions)