controlBMAgamma0 {ensembleBMA} | R Documentation |
Specifies a list of values controling the Bayesian Model Averaging fit of a mixture gammas with a point mass at 0 to ensemble forecasts for precipitation.
controlBMAgamma0(maxIter, eps, nEsteps, transformation, inverseTransformation, 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.
|
eps |
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 eps .
The default is sqrt(.Machine$double.eps) ,
which is approximately 1.e-8 on IEEE compliant machines.
|
nEsteps |
The number of E-steps per M-step. The default is 1, but using more E-steps per M-step will cause the convergence criterion to be satisfied in fewer iterations. This is a heuristic for a gain in speed since the results for multiple M-steps often are not much different from the results with one E-step per M-step. |
transformation |
The function corresponding to the transformation of the data used to fit the models for the point mass at 0 and mean of nonzero observations. The untransformed forecast is used to fit the variance model. |
inverseTransformation |
The function corresponding to the inverse of transformation .
Used for quantile forecasts and verification.
|
start |
An optional list of starting values for variance coefficients and weights. The default is to start with the variance coefficients equal to 1, and with equal weights for each member of the ensemble. |
A list whose components are the input arguments and their assigned values.
J. M. Sloughter, A. E. Raftery, T Gneiting and C. Fraley, Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Monthly Weather Review 135:3209–3220, 2007.
C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter,
ensembleBMA
: An R
Package for Probabilistic Ensemble Forecasting
using Bayesian Model Averaging,
Technical Report No. 516, Department of Statistics, University of
Washington, August 2007.
ensembleBMAgamma0
,
fitBMAgamma0
data(prcpTest) labels <- c("CENT","AVN","CMCG","ETA","GASP","JMA","NGPS","TCWB","UKMO") prcpTestData <- ensembleData( forecasts = prcpTest[ , labels], dates = prcpTest$date, observations = prcpTest$obs) prcpTestFitAlt <- ensembleBMAgamma0(prcpTestData, trainingRule=list(length=25,lag=2), control = controlBMAgamma0(maxIter = 10, transformation = function(x) x^(1/4), inverseTransformation = function(x) x^4))