fitBMAnormal {ensembleBMA} | R Documentation |
Fits a Baysian Model Averaging mixture of normals to ensemble forecasting data.
fitBMAnormal( ensembleData, control = controlBMAnormal())
ensembleData |
An ensembleData object with forecasts, observations and dates
for precipitation.
|
control |
A list of control values for the fitting functions. The defaults are
given by the function controlBMAnormal .
|
This function fits a BMA model to a training data set.
It is called by ensembleBMAnormal
, which produces a sequence
of fits over a larger precipitation data set.
The following methods are available for the output of fitBMA
:
gridForecastBMA
, quantileForecastBMA
, and
bmaModelParameters
.
A list with the following output components:
biasCoefs |
The fitted bias-correction coefficients. |
sd |
The fitted standard deviations for the mixture of normals model. |
weights |
The fitted weights for the mixture of normals model. |
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.
ensembleData
,
controlBMAnormal
,
ensembleBMAnormal
,
gridForecastBMA
,
quantileForecastBMA
,
bmaModelParameters
data(slp) slpData <- ensembleData(forecasts = slp[c("AVN","GEM","ETA","NGM","NOGAPS")], observations = slp$obs, dates = slp$date) DATE <- sort(unique(slpData$dates))[27] trainDat <- trainingData( slpData, date = DATE, trainingRule=list(length=25,lag=2)) slpFit25a <- fitBMA(trainDat, model = "normal") D <- as.numeric(slpData$dates) <= 25 slpFit25b <- fitBMA(slpData[D, ], model = "normal")