fitBMAnormal {ensembleBMA} | R Documentation |
Fits a Bayesian Model Averaging mixture of normals to a given training set.
fitBMAnormal( ensembleData, control = controlBMAnormal(), exchangeable = NULL)
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 controlBMAnormal .
|
exchangeable |
An optional numeric or character vector or factor indicating groups of
ensemble members that are exchangeable (indistinguishable).
The models have equal weights and parameters within each group.
If supplied, this argument will override any specification of
exchangeability in ensembleData .
|
This function fits a BMA model to a training data set.
It is called by ensembleBMAnormal
, which can produce a sequence
of fits over a larger data set.
Methods available for the output of fitBMAnormal
include:
cdf
, quantileForecast
, and modelParameters
.
A list with the following output components:
biasCoefs |
The fitted bias-correction coefficients. |
sd |
The fitted standard deviations for the mixture of normals model
(equal or varying across components according to the equalVariance
setting in the control input).
|
weights |
The fitted BMA weights for the normal components for each ensemble member. |
nIter |
The number of EM iterations. |
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 Ensembles and Bayesian Model Averaging,
Technical Report No. 516R, Department of Statistics, University of
Washington, 2009.
C. Fraley, A. E. Raftery, T. Gneiting, Using Bayesian Model Averaging to Calibrate Forecast Ensembles with Missing and Exchangeable Ensemble Members, (in preparation).
ensembleData
,
controlBMAnormal
,
ensembleBMAnormal
,
cdf
,
quantileForecast
,
modelParameters
data(ensBMAtest) ensNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo") obs <- paste("T2","obs", sep = ".") ens <- paste("T2", ensNames, sep = ".") tempTestData <- ensembleData( forecasts = ensBMAtest[,ens], observations = ensBMAtest[,obs], station = ensBMAtest[,"station"], dates = ensBMAtest[,"vdate"], forecastHour = 48, initializationTime = "00") tempTrain <- trainingData( tempTestData, trainingDays = 30, date = "2008010100") tempTrainFit <- fitBMAnormal( tempTrain) ## equivalent to ## tempTrainFit <- fitBMA( tempTrain, model = "normal")