ensembleBMAnormal {ensembleBMA} | R Documentation |
Fits a Bayesian Model Averaging mixture of normals to ensemble forecasts.
ensembleBMAnormal(ensembleData, dates = NULL, trainingRule = list(length=30, lag=2), control = controlBMAnormal(), warmStart = FALSE, minCRPS = FALSE)
ensembleData |
An ensembleData object including ensemble forecasts, observations
and dates of precipitation.
|
dates |
The dates for which modeling is desired. By default, this will include all dates consistent with the training rule. |
trainingRule |
A list giving the length and lag for the training period.
The default is to use a 30 time step training period for a forecast
2 time steps ahead of the last time step in the training period.
|
control |
A list of control values for the fitting functions. The defaults are
given by the function controlBMAnormal .
|
warmStart |
A logical variable indicating whether or not estimation of models for a sequence of dates or time steps should be initialized with the weights from the previous date or time step. The default is for the initilaizatiin to be independent of the result at the previous time step. |
minCRPS |
A logical variable indicating whether or not to add a postprocessing
step after the BMA fit to choose the standard deviation so as to
minimize the CRPS. The default is not to do the CRPS minimization,
which can add considerable extra cost to the computation, especially
when unequal variances are specified in control .
|
The output is for all of the dates
in ensembleData
, so there
will be missing entries denoted by NA
for dates that are too recent
to be forecast with the training rule.
The following methods are available for ensembleBMAnormal
objects:
gridForecastBMA
, quantileForecastBMA
,
bmaModelParameters
, brierSkillScores
, and crpsANDmae
.
A list with the following output components:
dateTable |
The table of observations corresponding to the dates in
ensembleData in chronological order.
|
trainingRule |
The training rule specified as input. |
biasCoefs |
The fitted bias-correction coefficients at each date. |
sd |
The fitted standard deviations for the mixture of normals model at each date. |
weights |
The fitted weights for the mixture of normals modelat each date. |
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
,
forecastBMAnormal
,
controlBMAnormal
,
fitBMAnormal
,
gridForecastBMA
,
quantileForecastBMA
,
bmaModelParameters
,
brierSkillScores
,
crpsANDmae
## Not run: data(slp) slpData <- ensembleData(forecasts = slp[c("AVN","GEM","ETA","NGM","NOGAPS")], observations = slp$obs, dates = slp$date) slpFit <- ensembleBMAnormal( slpData, minCRPS = TRUE) ## End(Not run)