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)