trainingData: 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 ensembleData object that includes ensemble forecasts, observations and dates.

trainingDays

An integer specifying the number of days in the training period.

consecutive

If TRUE then dates in training set are treated as consecutive, i.e. date gaps are ignored.

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.

Examples

Run this code

# NOT RUN {
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)
# }

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