controlBMAgamma {ensembleBMA} | R Documentation |
Specifies a list of values controling the Bayesian Model Averaging fit of a mixture of gammas to ensemble forecasts for wind speed.
controlBMAgamma(maxIter, tol, power = 1, start)
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. |
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. |
A list whose components are the input arguments and their assigned values.
J. M. Sloughter, T.Gneiting and A. E. Raftery, Probabilistic wind speed forecasting using ensembles and Bayesian model averaging, Technical Report No. 544, Department of Statistics, University of Washington, October 2008.
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.
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") winsTestFit1 <- ensembleBMAgamma(winsTestData, trainingDays = 30, control = controlBMAgamma(maxIter = 100, tol = 1.e-6))