trainingData {ensembleBMA} | R Documentation |
Extracts the training data corresponding to a given date and training rule.
trainingData( ensembleData, date, trainingRule = list(length=30, lag=2))
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.
|
The training rule uses the most recent days for the given period 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. 516, Department of Statistics, University of
Washington, August 2007.
## Not run: 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) ## End(Not run)