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 with ensemble forecasts, observations
and dates.
|
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:
cdfBMA
, 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
(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. 516, Department of Statistics, University of
Washington, August 2007.
ensembleData
,
controlBMAnormal
,
ensembleBMAnormal
,
cdfBMA
,
quantileForecastBMA
,
bmaModelParameters
data(slpTest) memberLabels <- c("AVN","GEM","ETA","NGM","NOGAPS") slpTestData <- ensembleData(forecasts = slpTest[ ,memberLabels], observations = slpTest$obs, dates = slpTest$date) DATE <- sort(unique(slpTestData$dates))[27] trainDat <- trainingData( slpTestData, date = DATE, trainingRule=list(length=25,lag=2)) ## Not run: slpTestFit <- fitBMA(trainDat, model = "normal") ## End(Not run) slpTestFit <- fitBMAnormal(trainDat)