Fits a Bayesian Modeling Averaging mixture of gammas.
Intended for wind speed forecasts.
Usage
fitBMAgamma( ensembleData, control = controlBMAgamma(), exchangeable = NULL)
Arguments
ensembleData
An ensembleData object including ensemble forecasts and
verification observations.
Missing values (indicated by NA) are allowed. Dates are ignored
if they are included. This is the training set for the model.
control
A list of control values for the fitting functions. The defaults are
given by the function controlBMAgamma.
exchangeable
An optional numeric or character vector or factor indicating groups of
ensemble members that are exchangeable (indistinguishable).
The model fit will have equal weights and parameters within each group.
If supplied, this argument will override any specification of
exchangeability in ensembleData.
Details
This function fits a BMA model to a training data set.
It is called by ensembleBMAgamma, which can produce a sequence
of fits over a larger precipitation data set.
Methods available for the output of fitBMA include:
cdf, quantileForecast, and
modelParameters.
Value
A list with the following output components:
biasCoefs
The fitted coefficients in the model for the mean of nonzero observations
for each member of the ensemble (used for bias correction).
varCoefs
The fitted coefficients for the model for the variance of nonzero
observations (these are the same for all members of the ensemble).
weights
The fitted BMA weights for the gamma components for each ensemble member.
nIter
The number of EM iterations.
power
A scalar value giving to the power by which the data was transformed
to fit the models for the point mass at 0 and the bias model.
The untransformed forecast is used to fit the variance model.
This is input as part of control.
References
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 Forecasting
using Ensembles and Bayesian Model Averaging,
Technical Report No. 516R, Department of Statistics, University of
Washington, 2007 (revised 2010).