The function computes the continuous rank probability score (CRPS). Note that the function uses numerical integration, for highly efficient computation please see the scoringRules package.

CRPS(object, newdata = NULL,
interval = c(-Inf, Inf), FUN = mean,
term = NULL, ...)

## Arguments

object

An object returned from bamlss.

newdata

Optional new data that should be used for calculation.

interval

The interval that should be used for numerical integration

FUN

Function to be applied on the CRPS scores.

term

If required, specify the model terms that should be used within the predict.bamlss function.

...

Arguments passed to function FUN.

## References

Gneiting T, Raftery AE (2007). Strictly Proper Scoring Rules, Prediction, and Estimation." Journal of the American Statistical Association, 102(477), 359--378. doi:10.1198/016214506000001437 cd ..

Gneiting T, Balabdaoui F, Raftery AE (2007). Probabilistic Forecasts, Calibration and Sharpness. Journal of the Royal Statistical Society B, 69(2), 243--268. doi:10.1111/j.1467-9868.2007.00587.x

## Examples

if (FALSE) ## Simulate data.
d <- GAMart()

## Model only including covariate x1.
b1 <- bamlss(num ~ s(x1), data = d)
#> Error in bamlss.model.frame(formula, data, family, weights, subset, offset,     na.action, specials, contrasts): object 'd' not found

## Now, also including x2 and x2.
b2 <- bamlss(num ~ s(x1) + s(x2) + s(x3), data = d)
#> Error in bamlss.model.frame(formula, data, family, weights, subset, offset,     na.action, specials, contrasts): object 'd' not found

## Compare using the CRPS score.
CRPS(b1)
#> Error in response.name(object, ...): object 'b1' not found
CRPS(b2)
#> Error in response.name(object, ...): object 'b2' not found