| pit {ensembleBMA} | R Documentation |
Computes the probabilty integral transform (PIT) of a BMA ensemble forecasting model at observation locations.
pit( fit, ensembleData, dates = NULL, ...)
fit |
A model fit to ensemble forecasting data. |
ensembleData |
An ensembleData object that includes ensemble forecasts,
verification observations and dates.
Missing values (indicated by NA) are allowed. \
This need not be the data used for the model 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 fit and dates.
|
dates |
The dates for which the CDF will be computed.
These dates must be consistent with fit and ensembleData.
The default is to use all of the dates in fit.
The dates are ignored if fit originates from fitBMA,
which also ignores date information.
|
... |
Included for generic function compatibility. |
Most often used for computing PIT histograms to assess calibration of
forecasts, in which case the observations in ensembleData awould
be those used in modeling fit.
Instances in ensembleData without verifying observations
are ignored.
Note the model may have been applied to a power transformation of the data,
but that information is included in the input fit, and
the output is transformed appropriately.
The PIT is a continuous analog of the verification rank.
The value of the BMA cumulative distribution function CDF
corresponding to the fit at the observed values in ensembleData.
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.
T. Gneiting, F. Balabdaoui and A. Raftery, Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society, Series B 69:243–268, 2007.
C. Fraley, A. E. Raftery, T. Gneiting and J. M. Sloughter,
ensembleBMA: An R Package for Probabilistic Forecasting
using Ensemble and Bayesian Model Averaging,
Technical Report No. 516R, Department of Statistics, University of
Washington, revised 2009.
ensembleBMA,
fitBMA,
quantileForecast,
verifRank,
data(ensBMAtest)
ensMemNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo")
obs <- paste("T2","obs", sep = ".")
ens <- paste("T2", ensMemNames, sep = ".")
tempTestData <- ensembleData( forecasts = ensBMAtest[,ens],
dates = ensBMAtest[,"vdate"],
observations = ensBMAtest[,obs],
station = ensBMAtest[,"station"],
forecastHour = 48,
initializationTime = "00")
## Not run:
# R check
tempTestFit <- ensembleBMAnormal( tempTestData, trainingDays = 30)
## End(Not run)
tempTestForc <- quantileForecast( tempTestFit, tempTestData)
range(tempTestForc)
tempTestPIT <- pit( tempTestFit, tempTestData,
values = seq(from=277, to=282.5, by = .1))
hist(tempTestPIT, breaks = 7)