controlBMAgamma {ensembleBMA}

Control parameters for BMA wind speed modeling

Description

Specifies a list of values controling the Bayesian Model Averaging fit of a mixture of gammas to ensemble forecasts for wind speed.

Usage

controlBMAgamma(maxIter, tol, power = 1, startupSpeed = NULL, init, 
           optim.control = list(ndeps = rep( sqrt(.Machine$double.eps), 2)))

Arguments

maxIter

An integer specifying an upper limit on the number of iterations' for fitting the BMA mixture via EM. The default is Inf, which sets no upper limit on the number of iterations, so that the convergence criterion based on eps is used.

tol

A numeric convergence tolerance. The EM fit for the mixture of gammas is terminated when the relative error in successive objective values in the M-step falls below tol. The default is sqrt(.Machine$double.eps), which is approximately 1.e-8 on IEEE compliant machines.

power

A scalar value giving the power by which the data will be transformed to fit the model for mean of the observations. The default is not to transform the data. The untransformed forecast is used to fit the variance model.

startupSpeed

A scalar value giving a global value for the anemometer startup speed, or the threshold below which a value of 0 is recorded. As this can vary from station to station and network to network, it may be preferable to include startupSpeed as part of the ensembleData object.

init

An optional list of initial values for variance coefficients and weights. The default is to start with the variance coefficients equal to 1, and with equal weights for each member of the ensemble.

optim.control

Control parameters for the optim function used in the M-step of EM. The default here is list(ndeps = rep( sqrt(.Machine$double.eps), 2)), which assigns a smaller finite-difference step size than the optim default of 1.e-3. To use the default control parameters for optim, set optim.control=NULL.

Value

A list whose components are the input arguments and their assigned values.

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 Ensemble Forecasting using Bayesian Model Averaging, Technical Report No. 516R, Department of Statistics, University of Washington, 2007 (revised 2010).

See Also

ensembleBMAgamma, fitBMAgamma

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"],
                                forecastHour = 48,
                                initializationTime = "00")

## Not run:  # R check
   winsTestFit1 <- ensembleBMAgamma(winsTestData, trainingDays = 30,
       control = controlBMAgamma(maxIter = 100, tol = 1.e-6,
   startupSpeed =1))

## End(Not run)

# for quick run only; use more training days for forecasting
   winsTestFit1 <- ensembleBMAgamma(winsTestData[1:14,], trainingDays = 5,
      control = controlBMAgamma(maxIter = 100, tol = 1.e-6, startupSpeed = 1))