# BrierDecomp: Brier Score decomposition

## 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)

## Value

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

## 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.

## References

Murphy (1973): A New Vector Partition of the Probability Score. J. Appl. Met. 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. 10.1002/qj.1924

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

## See Also

ReliabilityDiagram

## Examples

# NOT RUN {
data(eurotempforecast)
BrierDecomp(rowMeans(ens.bin), obs.bin, bins=3, bias.corrected=TRUE)
# }