BrierDecomp: Brier Score decomposition

In sieste/SpecsVerification: Forecast Verification Routines for Ensemble Forecasts of Weather and Climate

Description

Return decomposition of the Brier Score into Reliability, Resolution and Uncertainty, and estimated standard deviations

Usage

BrierDecomp(p, y, bins = 10, bias.corrected = FALSE)

Arguments

p	vector of forecast probabilities
y	binary observations, y[t]=1 if an event happens at time t, and y[t]=0 otherwise
bins	binning to estimate the calibration function (see Details), default: 10
bias.corrected	logical, default=FALSE, whether the standard (biased) decomposition of Murphy (1973) should be used, or the bias-corrected decomposition of Ferro (2012)

Details

To estimate the calibration curve, the unit line is categorised into discrete bins, provided by the 'bins' argument. If 'bins' is a single number, it specifies the number of equidistant bins. If 'bins' is a vector of values between zero and one, these values are used as the bin-breaks.

Value

Estimators of the three components and their estimated standard deviations are returned as a 2*3 matrix.

References

Murphy (1973): A New Vector Partition of the Probability Score. J. Appl. Met. doi: 10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2

Ferro and Fricker (2012): A bias-corrected decomposition of the Brier score. QJRMS. doi: 10.1002/qj.1924

Siegert (2013): Variance estimation for Brier Score decomposition. QJRMS. doi: 10.1002/qj.2228

See Also

ReliabilityDiagram

Examples

data(eurotempforecast)
BrierDecomp(rowMeans(ens.bin), obs.bin, bins=3, bias.corrected=TRUE)

sieste/SpecsVerification documentation built on May 29, 2019, 9:59 p.m.