ensembleBMA (version 2.0)

controlBMAnormal: Control parameters for BMA for precipitation.

Description

Specifies a list of values controling the Bayesian Model Averaging fit of a mixture of normals to ensemble forecasts.

Usage

controlBMAnormal(maxIter, eps, equalVariance, biasCorrection, start)

Arguments

maxIter

An integer specifying an upper limit on the number of iterations for fitting the BMA mixture via EM. The default is .Machine$integer.max, which essentially sets no upper limit on the number of iterations, so that the convergenc

eps

A numeric convergence tolerance. The EM fit for the mixture model is terminated when the relative error in successive objective values in the M-step falls below eps. The default is sqrt(.Machine$double.eps),

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. [object Object],[object Object],[object Object]

start

An optional list of starting 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.

See Also

ensembleBMAnormal, forecastBMAnormal, fitBMAnormal

Examples

data(slp)

  slpData <- ensembleData(forecasts = slp[c("AVN","GEM","ETA","NGM","NOGAPS")],
                          observations = slp$obs, dates = slp$date)

  slpFit <- ensembleBMAnormal(slpData, 
    control = controlBMAnormal(maxIter = 100, 
                               equalVariance = FALSE,
                               biasCorrection = "additive"))