ensembleBMA (version 2.1)

trainingData: Extract Training Data

Description

Extracts the training data corresponding to a given date and training rule.

Usage

trainingData( ensembleData, date, trainingRule = list(length=30, lag=2))

Arguments

ensembleData

An ensembleData object including ensemble forecasts, observations and dates.

date

The date for which the training data is desired.

trainingRule

A list giving the length and lag for the training period. The default is to use a 30 day training period for a forecast 2 days ahead of the last day in the training period.

Value

• An ensembleData object corresponding to the training data for the given date relative to ensembleData.

Details

The training rule uses the most recent days for the given period regardless of whether or not they are consecutive.

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. 516, Department of Statistics, University of Washington, August 2007.

See Also

ensembleBMA, fitBMA

Examples

data(slpTest)

  memberLabels <- c("AVN","GEM","ETA","NGM","NOGAPS")
  slpTestData <- ensembleData( forecasts = slpTest[ ,memberLabels],
                         observations = slpTest$obs, dates = slpTest$date)

  trainDat <- trainingData(slpTestData, date = "2000063000",
                           trainingRule = list(length=30,lag =2))
 
  slpTestFitTD <- fitBMAnormal(trainDat)