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Scaled Ranked Probability Score (SRPS, SCRPS)

Source: R/pred_measure-builtin.R
measure_srps.Rd

A convenience wrapper around measure_rps() with scaled = TRUE. Computes the scaled ranked probability score (SRPS) for discrete outcomes or the scaled continuously ranked probability score (SCRPS) for continuous outcomes. Scaling makes the score invariant to the spread of the predictive distribution, which can be useful when comparing models across observations with very different predictive uncertainties.

Usage

measure_srps(y, ypred, log_weights = NULL, pointwise = NULL)

Arguments

y

A numeric vector of \(n\) observed outcomes. May be integer-valued (for RPS/SRPS) or continuous (for CRPS/SCRPS).

ypred

A numeric matrix of posterior predictive draws with dimensions \(S \times n\) (draws × observations).

log_weights

Optional numeric matrix of unnormalized log-importance weights with dimensions \(S \times n\). Weights are column-normalized before computing each per-observation contribution.

pointwise

Optional numeric vector of precomputed pointwise values, on the scale of the measure that is returned: RPS/CRPS when scaled = FALSE and SRPS/SCRPS when scaled = TRUE. The values are used as they are, with no further transformation; in particular, unscaled values are not converted to the scaled variant. If provided, y, ypred, and log_weights are ignored.

Value

An object of class "measure": a list with:

estimates

A 1 \(\times\) 2 matrix. The row is named after the measure, the columns are Estimate and SE (standard error).

pointwise

An \(n \times 1\) matrix of per-observation values. The column is named after the measure.

extra

Optional. Auxiliary data for the standard error of a difference. Most measures do not set it.

Attributes measure (i.e., measure name) and dims (draws \(\times\) observations) are also set. Use print() for a readable summary.

Details

See measure_rps() for full details on arguments, estimation, and references.

Examples

y <- c(2L, 1L, 3L)
ypred <- matrix(c(2, 1, 2, 3, 1, 3), nrow = 2)
measure_srps(y, ypred)
#> 
#> Computed from 2 draws by 3 observations.
#> 
#>      Estimate    SE
#> srps    -0.95  0.29