trainingData {ensembleMOS} | R Documentation |
Extract Training Data
Description
Extracts a subset of an ensembleData
object corresponding
to a given date and number of training days.
Usage
trainingData(ensembleData, trainingDays, consecutive = FALSE, date)
Arguments
ensembleData |
An |
trainingDays |
An integer specifying the number of days in the training period. |
consecutive |
If |
date |
The date for which the training data is desired. |
Value
An ensembleData
object corresponding to the training data for
the given date relative to
ensembleData
.
References
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.
Available at: http://www.stat.washington.edu/research/reports/
C. Fraley, A. E. Raftery and T. Gneiting, Calibrating multi-model forecast ensembles with exchangeable and missing members using Bayesian model averaging, Monthly Weather Review 138:190-202, 2010.
See Also
ensembleMOSnormal
,
fitMOSnormal
Examples
data("ensBMAtest", package = "ensembleBMA")
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")
tempTrain <- trainingData(tempTestData, trainingDays = 30,
date = "2008010100")
tempTrainFit <- fitMOSnormal(tempTrain)