ensembleBMA (version 5.1.8)

fitBMAgamma: BMA wind speed model fit to a training set

Description

Fits a Bayesian Modeling Averaging mixture of gammas. Intended for wind speed forecasts.

Usage

fitBMAgamma( ensembleData, control = controlBMAgamma(), exchangeable = NULL)

Value

A list with the following output components:

biasCoefs

The fitted coefficients in the model for the mean of nonzero observations for each member of the ensemble (used for bias correction).

varCoefs

The fitted coefficients for the model for the variance of nonzero observations (these are the same for all members of the ensemble).

weights

The fitted BMA weights for the gamma components for each ensemble member.

nIter

The number of EM iterations.

power

A scalar value giving to the power by which the data was transformed to fit the models for the point mass at 0 and the bias model. The untransformed forecast is used to fit the variance model. This is input as part of control.

Arguments

ensembleData

An ensembleData object including ensemble forecasts and verification observations. Missing values (indicated by NA) are allowed. Dates are ignored if they are included. This is the training set for the model.

control

A list of control values for the fitting functions. The defaults are given by the function controlBMAgamma.

exchangeable

An optional numeric or character vector or factor indicating groups of ensemble members that are exchangeable (indistinguishable). The model fit will have equal weights and parameters within each group. If supplied, this argument will override any specification of exchangeability in ensembleData.

Details

This function fits a BMA model to a training data set.
It is called by ensembleBMAgamma, which can produce a sequence of fits over a larger precipitation data set.
Methods available for the output of fitBMA include: cdf, quantileForecast, and modelParameters.

References

J. M. Sloughter, T. Gneiting and A. E. Raftery, Probabilistic wind speed forecasting using ensembles and Bayesian model averaging, Journal of the American Statistical Association, 105:25--35, 2010.

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, 2007 (revised 2010).

See Also

ensembleData, controlBMAgamma, ensembleBMAgamma, cdf, quantileForecast, modelParameters

Examples

  data(ensBMAtest)

  ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")
  
  obs <- paste("MAXWSP10","obs", sep = ".")
  ens <- paste("MAXWSP10", ensMemNames, sep = ".")

  winsTestData <- ensembleData( forecasts = ensBMAtest[,ens],
                                dates = ensBMAtest[,"vdate"], 
                                observations = ensBMAtest[,obs],
                                station = ensBMAtest[,"station"],
                                startupSpeed = 1,
                                forecastHour = 48,
                                initializationTime = "00")

if (FALSE)  # R check
  winsTrain <- trainingData( winsTestData, trainingDays = 30,
                             date = "2008010100")


# for quick run only; use more training days for forecasting
  winsTrain <- trainingData( winsTestData, trainingDays = 10,
                             date = "2008010100")

  winsTrainFit <- fitBMAgamma( winsTrain)

## equivalent to
##    winsTrainFit <- fitBMA( winsTrain, model = "gamma")