Control parameters for BMA mixtures of normals
Description
Specifies a list of values controling the Bayesian Model Averaging fit of a mixture of normals to ensemble forecasts.
Usage
1  controlBMAnormal(maxIter, tol, equalVariance, biasCorrection, init)

Arguments
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
model is terminated when the relative error in successive
objective values in the Mstep falls below 
equalVariance 
A logical value indicating whether or not the variances for the mixture components should to be equal. The default is to constrain them to be equal. 
biasCorrection 
A character string describing the type of bias correction to be used.

init 
An optional list of initial values for standard deviations and weights. The default is to start with all standard deviations 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
A. E. Raftery, T. Gneiting, F. Balabdaoui and M. Polakowski, Using Bayesian model averaging to calibrate forecast ensembles, Monthly Weather Review 133:1155–1174, 2005.
C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter,
ensembleBMA
: An R
Package for Probabilistic Forecasting
using Ensemble and Bayesian Model Averaging,
Technical Report No. 516R, Department of Statistics, University of
Washington, 2007 (revised 2010).
See Also
ensembleBMAnormal
,
fitBMAnormal
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  data(ensBMAtest)
ensMemNames < c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")
obs < paste("T2","obs", sep = ".")
ens < paste("T2", ensMemNames, sep = ".")
tempTestData < ensembleData( forecasts = ensBMAtest[,ens],
dates = ensBMAtest[,"vdate"],
observations = ensBMAtest[,obs],
station = ensBMAtest[,"station"],
forecastHour = 48,
initializationTime = "00")
## Not run: # R check
tempTestFit1 < ensembleBMAnormal(tempTestData, trainingDays = 30,
control = controlBMAnormal(maxIter = 100, biasCorrection = "additive"))
## End(Not run)
# for quick run only; use more training days for forecasting
tempTestFit1 < ensembleBMAnormal(tempTestData[1:20,], trainingDays = 5,
control = controlBMAnormal(maxIter = 100, biasCorrection = "additive"))
