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This function is a wrapper around the
logs_sample() function from the
scoringRules package.
The log score is the negative logarithm of the predictive density evaluated at the observed value.
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 function uses a kernel density estimation, which is not well defined with integer-valued Monte Carlo Samples. See the scoringRules package for more details and alternatives, e.g. calculating scores for specific discrete probability distributions.
logs_sample(observed, predicted, ...)Vector with scores.
A vector with observed values of size n
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.
Overview of required input format for sample-based forecasts
Alexander Jordan, Fabian Krüger, Sebastian Lerch, Evaluating Probabilistic Forecasts with scoringRules, https://www.jstatsoft.org/article/view/v090i12
Other log score functions:
logs_categorical(),
scoring-functions-binary
observed <- rpois(30, lambda = 1:30)
predicted <- replicate(200, rpois(n = 30, lambda = 1:30))
logs_sample(observed, predicted)
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