fitBMAnormal {ensembleBMA}R Documentation

BMA for a mixture of normals.

Description

Fits a Baysian Model Averaging mixture of normals to ensemble forecasting data.

Usage

fitBMAnormal( ensembleData, control = controlBMAnormal()) 

Arguments

ensembleData An ensembleData object with forecasts, observations and dates for precipitation.
control A list of control values for the fitting functions. The defaults are given by the function controlBMAnormal.

Details

This function fits a BMA model to a training data set.
It is called by ensembleBMAnormal, which produces a sequence of fits over a larger precipitation data set.
The following methods are available for the output of fitBMA: gridForecastBMA, quantileForecastBMA, and bmaModelParameters.

Value

A list with the following output components:

biasCoefs The fitted bias-correction coefficients.
sd The fitted standard deviations for the mixture of normals model.
weights The fitted weights for the mixture of normals model.
nIter The number of EM iterations.

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.

See Also

ensembleData, controlBMAnormal, ensembleBMAnormal, gridForecastBMA, quantileForecastBMA, bmaModelParameters

Examples

  data(slp)

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

  DATE <- sort(unique(slpData$dates))[27]
  trainDat <- trainingData( slpData, date = DATE, trainingRule=list(length=25,lag=2))
  slpFit25a <- fitBMA(trainDat, model = "normal")

  D <- as.numeric(slpData$dates) <= 25
  slpFit25b <- fitBMA(slpData[D, ], model = "normal")

[Package ensembleBMA version 2.0 Index]