quantileForecastBMA {ensembleBMA} | R Documentation |
Computes quantiles for the probability distribution function (PDF) for an ensemble BMA mixture model at observation locations.
quantileForecastBMA( fit, ensembleData, quantiles = 0.5, dates = NULL, popData = NULL, ...)
fit |
An ensemble BMA model fit. |
ensembleData |
An ensembleData object including ensemble
forecasts and observations. It need not be the object
used to form fit , although it must include the same
ensemble members. If ensembleData includes dates,
they must be consistent with fit and dates .
If ensembleData does not include dates, they will
be inferred from the fit and the dates argument.
|
quantiles |
The vector of desired quantiles for the PDF of the BMA mixture model. |
dates |
The dates for which the quantile forecasts will be computed.
These dates must be consistent with fit and ensembleData .
The default is to use all of the dates in fit .
|
popData |
For gamma0 model fits, there is an additional popData
argument for providing predictors in the logistic regression for
probability of zero precipitation. If popData was supplied to
obtain in the modeling for fit , then popData of the
same kind must be supplied here.
|
... |
Included for generic function compatibility. |
This method is generic, and can be applied to any ensemble BMA forecasting
model.
Note the model may have been applied to a transformation of the data,
but that information is included in the input fit
, and
the output is transformed appropriately.
This can be used to compute prediction intervals for the PDF.
A vector of forecasts corresponding to the desired quantiles.
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.
J. M. Sloughter, A. E. Raftery, T. Gneiting and C. Fraley, Probabilistic quantitative precipitation forecasting using Bayesian model averaging, Monthly Weather Review 135:3209–3220, 2007.
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.
data(slpTest) labels <- c("AVN","GEM","ETA","NGM","NOGAPS") slpTestData <- ensembleData( forecasts = slpTest[ ,labels], observations = slpTest$obs, dates = slpTest$date) slpTestFit <- ensembleBMAnormal(slpTestData) slpTestForc <- quantileForecastBMA( slpTestFit, slpTestData) ## Not run: data(srft) labels <- c("CMCG","ETA","GASP","GFS","JMA","NGPS","TCWB","UKMO") srftData <- ensembleData( forecasts = srft[ ,labels], dates = srft$date, observations = srft$obs, latitude = srft$lat, longitude = srft$lon) srftFit <- ensembleBMAnormal(srftData, date = "2004012900") data(srftGrid) srftGridData <- ensembleData(forecasts = srftGrid[ ,labels], latitude = srftGrid$lat, longitude = srftGrid$lon) srftGridForc <- quantileForecastBMA( srftFit, srftGridData, date = "2004012900") ## End(Not run)