View source: R/controlBMAgamma.R
controlBMAgamma  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, startupSpeed = NULL, init, optim.control = list(ndeps = rep( sqrt(.Machine$double.eps), 2)))
maxIter 
An integer specifying an upper limit on the number of iterations'
for fitting the BMA mixture via EM. The default is

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 Mstep falls below 
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 
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 Mstep of EM.
The default here is list(ndeps = rep( sqrt(.Machine$double.eps), 2)),
which assigns a smaller finitedifference step size than the

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, 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).
ensembleBMAgamma
,
fitBMAgamma
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.e6, 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.e6, startupSpeed = 1))
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