controlBMAgamma0: Control parameters for BMA precipitation modeling

Description

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

Usage

controlBMAgamma0(maxIter, eps, nEsteps, transformation, inverseTransformation, 
                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

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 eps. The default is sqrt(.Machine$double.eps),

nEsteps

The number of E-steps per M-step. The default is 1, but using more E-steps per M-step will cause the convergence criterion to be satisfied in fewer iterations. This is a heuristic for a gain in speed since the results for multiple M-steps

transformation

The function corresponding to the transformation of the data used to fit the models for the point mass at 0 and mean of nonzero observations. The untransformed forecast is used to fit the variance model.

inverseTransformation

The function corresponding to the inverse of transformation. Used for quantile forecasts and verification.

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. 516, Department of Statistics, University of Washington, August 2007.

Examples

Run this code

data(prcpTest)

  labels <- c("CENT","AVN","CMCG","ETA","GASP","JMA","NGPS","TCWB","UKMO")
  prcpTestData <- ensembleData( forecasts = prcpTest[ , labels],
                          dates = prcpTest$date, observations = prcpTest$obs)

  prcpTestFitAlt <- ensembleBMAgamma0(prcpTestData, 
                         trainingRule=list(length=25,lag=2),
                       control = controlBMAgamma0(maxIter = 10, 
                              transformation = function(x) x^(1/4),
                              inverseTransformation = function(x) x^4))