controlBMAgamma0 {ensembleBMA}R Documentation

Control parameters for BMA precipitation modeling

Description

Specifies a list of values controling the Bayesian Model Averaging fit of a mixture of gammas with a point mass at 0 to ensemble forecasts for precipitation.

Usage

controlBMAgamma0(maxIter, tol, nEsteps, power = (1/3), start)

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.
nEsteps The number of E-steps per M-step, after five iterations of standard EM. The default is 1, but using more E-steps per M-step causes the convergence criterion to be satisfied in fewer iterations. This is a heuristic to speed things up since the results for multiple E-steps often are not much different from the results with one E-step per M-step.
power A scalar value giving the power by which the data will be transformed to fit the models for the point mass at 0 and mean of nonzero observations. The default is to use the 1/3 power of the data. The untransformed forecast is used to fit the variance model.
start An optional list of starting 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.

Value

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

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 Ensemble Forecasting using Bayesian Model Averaging, Technical Report No. 516R, Department of Statistics, University of Washington, 2009.

See Also

ensembleBMAgamma0, fitBMAgamma0

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")

  prcpTestFit1 <- ensembleBMAgamma0( prcpTestData, trainingDays = 30,
       control = controlBMAgamma0(power = (1/4)))

[Package ensembleBMA version 4.2 Index]