View source: R/controlBMAgamma0.R
controlBMAgamma0  R Documentation 
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.
controlBMAgamma0(maxIter = Inf, tol = sqrt(.Machine$double.eps), power = (1/3), rainobs = 10, init = list(varCoefs = NULL, weights = NULL), optim.control = list(ndeps = rep( sqrt(.Machine$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 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. 
rainobs 
An integer specifying a minimum number of observations with nonzero precipitation in the training data. When necessary and possible, the training period will be extended backward in increments of days to meet the minimum requirement. It is not possible to fit the BMA model for precipitation without sufficient nonzero observations. The default minimum number is 10. It many instances fewer nonzero observations may suffice, but it could also be that more are needed to model precipitation in some datasets. 
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, 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, 2007 (revised 2010).
ensembleBMAgamma0
,
fitBMAgamma0
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") ## Not run: # R check prcpTestFit1 < ensembleBMAgamma0( prcpTestData, trainingDays = 30, control = controlBMAgamma0(power = (1/4))) ## End(Not run) # for quick run only; use more training days for forecasting prcpTestFit1 < ensembleBMAgamma0( prcpTestData[1:14,], trainingDays = 6, control = controlBMAgamma0(power = (1/4)))
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