crpsNormal {ensembleBMA} | R Documentation |
Computes the continuous ranked probability score for a normal ensemble BMA forecasting model.
crpsNormal( sd, weights, biasCoefs, ensembleData)
sd |
Standard deviation for an ensemble BMA forecasting model. This will be a scalar if the variances are assumed equal across the ensemble, and otherwise a vector. |
weights |
Weights for the ensemble BMA forecasting model. |
biasCoefs |
Bias coefficients for the ensemble BMA forecasting model. |
ensembleData |
An ensembleData object giving including ensemble
forecasts and observations associated with the ensemble BMA model.
|
This function is mainly intended for internal use by ensembleBMA
and forecastBMA
with the minCRPS
option.
The mean of the continuous ranked probability scores for
ensembleData
according to the ensemble BMA model
specified by sd
, weights
and biasCoefs
.
D. A. Unger, A method to estimate the continuous ranked probability score, Preprints of the Ninth Conference on Probability and Statistics in Atmospheric Sciences, Virginia Beach, VA USA, 206-213, American Meteorological Society.
H. Hersbach, Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather and Forecasting 15, 559-570.
T. Gneiting and A. E. Raftery, Strictly proper scoring rules, prediction and estimation, Technical Report No. 463R, Department of Statistics, University of Washington, November 2006 (to appear in Journal of the American Statistical Association).
ensembleBMAnormal
,
forecastBMAnormal
data(slp) slpData <- ensembleData(forecasts = slp[c("AVN","GEM","ETA","NGM","NOGAPS")], observations = slp$obs, dates = slp$date) ## default training data for the 32nd date trainDat <- trainingData(slpData, date = sort(unique(slpData$dates))[32], trainingRule = list(length=30, lag=2)) slpFitTD <- fitBMAnormal(trainDat) slp32 <- slpData[sort(unique(slpData$dates))[32], ] crpsNormal( slpFitTD$sd, slpFitTD$weights, slpFitTD$biasCoefs, slp32)