fitBMAgamma0 {ensembleBMA}

BMA precipitation model fit to a training set

Description

Fits a Bayesian Modeling Averaging mixture of gammas with a point mass at 0 to a given training set. Intended for precipitation forecasts.

Usage

fitBMAgamma0( ensembleData, control = controlBMAgamma0(), 
              exchangeable = NULL) 

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 controlBMAgamma0.

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 ensembleBMAgamma0, 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.

Value

A list with the following output components:

prob0coefs

The fitted coefficients in the model for the point mass at 0 (probability of zero precipitation) for each member of the ensemble.

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.

References

J. M. Sloughter, A. E. Raftery, T. Gneiting and C. Fraley, Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Monthly Weather Review 135:3209–3220, 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, 2007 (revised 2010).

C. Fraley, A. E. Raftery, T. Gneiting, Calibrating Multi-Model Forecast Ensembles with Exchangeable and Missing Members using Bayesian Model Averaging, Monthly Weather Review 138:190–202, 2010.

See Also

ensembleData, controlBMAgamma0, ensembleBMAgamma0, cdf, quantileForecast, modelParameters

Examples

  data(ensBMAtest)

  ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")

  obs <- paste("PCP24","obs", sep = ".")
  ens <- paste("PCP24", ensMemNames, sep = ".")

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

## Not run:  # R check
  prcpTrain <- trainingData( prcpTestData, trainingDays = 30,
                             date = "2008010100")

## End(Not run)

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

  prcpTrainFit <- fitBMAgamma0( prcpTrain)

## equivalent to
##    prcpTrainFit <- fitBMA( prcpTrain, model = "gamma0")