trainingData {ensembleBMA} | R Documentation |
Extracts a subset of an ensembleData
object corresponding
to a given date and number of training days.
trainingData( ensembleData, trainingDays, date)
ensembleData |
An ensembleData object that includes, ensemble
forecasts, observations and dates.
|
trainingDays |
An integer specifying the number of days in the training period.
|
date |
The date for which the training data is desired. |
The most recent days are used for training regardless of whether or not they are consecutive.
An ensembleData
object corresponding to the training data for
the given date relative to ensembleData
.
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 135:3309–3320, 2007.
C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter,
ensembleBMA
: An R
Package for Probabilistic Forecasting
using Ensembles and Bayesian Model Averaging,
Technical Report No. 516R, Department of Statistics, University of
Washington, December 2008.
C. Fraley, A. E. Raftery, T. Gneiting, Using Bayesian Model Averaging to Calibrate Forecast Ensembles with Missing and Exchangeable Ensemble Members, (in preparation).
data(ensBMAtest) ensNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo") obs <- paste("T2","obs", sep = ".") ens <- paste("T2", ensNames, sep = ".") tempTestData <- ensembleData( forecasts = ensBMAtest[,ens], observations = ensBMAtest[,obs], station = ensBMAtest[,"station"], dates = ensBMAtest[,"vdate"], forecastHour = 48, initializationTime = "00") tempTrain <- trainingData( tempTestData, trainingDays = 30, date = "2008010100") tempTrainFit <- fitBMAnormal( tempTrain)