cdf {ensembleBMA} | R Documentation |
Computes the cumulative distribution function (CDF) of an ensemble forecasting model at observation locations.
cdf( fit, ensembleData, values, dates = NULL, ...)
fit |
A model fit to ensemble forecasting data. |
ensembleData |
An ensembleData object that includes ensemble forecasts,
verification observations and dates.
Missing values (indicated by NA ) are allowed. \
This need not be the data used for the model fit ,
although it must include the same ensemble members. \
If ensembleData includes dates,
they must be consistent with fit and dates .
If ensembleData does not include dates, they will
be inferred from fit and dates .
|
values |
The vector of desired values at which the CDF of the ensemble forecasting model is to be evaluated. |
dates |
The dates for which the CDF will be computed.
These dates must be consistent with fit and ensembleData .
The default is to use all of the dates in fit .
The dates are ignored if fit originates from fitBMA ,
which also ignores date information.
|
... |
Included for generic function compatibility. |
This method is generic, and can be applied to any ensemble forecasting
model.
Note the model may have been applied to a power transformation of the data,
but that information is included in the input fit
, and
the output is transformed appropriately.
A vector of probabilities corresponding to the CDF at the desired values. Useful for determining propability of freezing, precipitation, etc.
A. E. Raftery, T. Gneiting, F. Balabdaoui and M. Polakowski, Using Bayesian model averaging to calibrate forecast ensembles, Monthly Weather Review 133:1155–1174, 2005.
J. M. Sloughter, A. E. Raftery, T. Gneiting and C. Fraley, Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Monthly Weather Review 135:3209–3220, 2007.
C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter,
ensembleBMA
: An R
Package for Probabilistic Forecasting
using Ensemble and Bayesian Model Averaging,
Technical Report No. 516R, Department of Statistics, University of
Washington, 2009.
C. Fraley, A. E. Raftery, T. Gneiting, Using Bayesian Model Averaging to Calibrate Forecast Ensembles with Missing and Exchangeable Ensemble Members, (in preparation).
ensembleBMA
,
fitBMA
,
quantileForecast
data(ensBMAtest) ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo") obs <- paste("T2","obs", sep = ".") ens <- paste("T2", ensMemNames, sep = ".") tempTestData <- ensembleData( forecasts = ensBMAtest[,ens], dates = ensBMAtest[,"vdate"], observations = ensBMAtest[,obs], station = ensBMAtest[,"station"], forecastHour = 48, initializationTime = "00") ## Not run: # R check tempTestFit <- ensembleBMAnormal( tempTestData, trainingDays = 30) ## End(Not run) tempTestForc <- quantileForecast( tempTestFit, tempTestData) range(tempTestForc) tempTestCDF <- cdf( tempTestFit, tempTestData, values = seq(from=277, to=282, by = 1)) tempTestCDF