qscore: Quantile scores for IDR or raw forecasts
In isodistrreg: Isotonic Distributional Regression (IDR)

Description Usage Arguments Details Value References See Also Examples

Computes quantile scores of IDR quantile predictions or of quantile predictions from raw forecasts in a data.frame.

1	qscore(predictions, quantiles, y)

`predictions`	either an object of class `idr` (output of `predict.idrfit`), or a `data.frame` of numeric variables. In the latter case, quantiles are computed using the empirical distribution of the variables in `predictions`.
`quantiles`	numeric vector of desired quantiles.
`y`	a numeric vector of obervations of the same length as the number of predictions, or of length 1. In the latter case, `y` will be used for all predictions.

The quantile score of a forecast x for the u-quantile is defined as

2(1{x > y} - u)(x - y),

where y is the observation. For u = 1/2, this equals the mean absolute error of the median forecast.

A matrix of the quantile scores for the desired quantiles, one column per quantile.

Gneiting, T. and Raftery, A. E. (2007), 'Strictly proper scoring rules, prediction, and estimation', Journal of the American Statistical Association 102(477), 359-378

predict.idrfit, qpred

data("rain")

## Postprocess HRES forecast using data of 3 years

X <- rain[1:(3 * 365), "HRES", drop = FALSE]
y <- rain[1:(3 * 365), "obs"]

fit <- idr(y = y, X = X)

## Compute mean absolute error of the median postprocessed forecast using
## data of the next 2 years (out-of-sample predictions) and compare to raw
## HRES forecast

data <- rain[(3 * 365 + 1):(5 * 365), "HRES", drop = FALSE]
obs <- rain[(3 * 365 + 1):(5 * 365), "obs"]

predictions <- predict(fit, data = data)
idrMAE <- mean(qscore(predictions, 0.5, obs))
rawMAE <- mean(qscore(data, 0.5, obs))

c("idr" = idrMAE, "raw" = rawMAE)