crpsANDmae {ensembleBMA} | R Documentation |
Computes the continuous ranked probability score and median absolute error for ensemble BMA forecasting models via sampling. Can be used for distributions for which the CDF is not available.
crpsANDmae( object, ensembleData, nSamples = 10000, seed = NULL, popData = NULL, ...)
object |
An ensemble BMA model fit for ensembleData .
|
ensembleData |
An ensembleData object giving including ensemble
forecasts, observations and dates corresponding to object .
|
nSamples |
The number of simulation samples (gamma0 for precipitation model only) |
seed |
Argument to set.seed for random number generation in simulation.
|
popData |
For ensembleBMAgamma0 and fitBMAgamma0 objects, there
is an additional popData argument for providing predictors
in the logistic regression for probability of precipitation.
The value of popData (if any) used to create object
should be supplied here.
|
... |
Included for generic function compatibility. |
This method is generic, and can be applied to all ensemble BMA forecasting
models.
Note the model may have been applied to a transformation of the data,
but that information is included in the input x
, and
the output is transformed appropriately.
For the gamma0 model for precipitation, the CRPS is only available
through simulation, because a transformation of the forecast values
is being modeled rather than the forecast values themselves (the
default transformation is the cube root). For normal models for
temperature and pressure, simulation is not need and the
nSamples
and seed
arguments are ignored.
A matrix whose rows correspond to the crps
and
mae
, respectively, and whose columns correpond
to these values for climatology, the ensemble,
the BMA model, and simulations from the BMA model.
D. A. Unger, A method to estimate the continuous ranked probability score, Preprints of the Ninth Conference on Probability and Statistics in Atmospheric Sciences, Virginia Beach, VA USA, 206-213, American Meteorological Society.
H. Hersbach, Decomposition of the continuous ranked probability score for ensemble prediction systems, Weather and Forecasting 15, 559-570.
T. Gneiting and A. E. Raftery, Strictly proper scoring rules, prediction and estimation, Technical Report No. 463R, Department of Statistics, University of Washington, November 2006 (to appear in Journal of the American Statistical Association).
data(slp) slpData <- ensembleData(forecasts = slp[c("AVN","GEM","ETA","NGM","NOGAPS")], observations = slp$obs, dates = slp$date) slpFit <- ensembleBMAnormal(slpData) crpsANDmae( slpFit, slpData)