crps | R Documentation |

Computes the continuous ranked probability score (CRPS) for univariate ensemble forecasting models.

crps( fit, ensembleData, dates=NULL, nSamples=NULL, seed=NULL, ...) CRPS( fit, ensembleData, dates=NULL, nSamples=NULL, seed=NULL, ...)

`fit` |
A model fit to ensemble forecasting data. |

`ensembleData` |
An |

`nSamples` |
The number of simulation samples for CRPS via simulation.
For the normal model, the default is analytic computation of the CRPS.
For the gamma model with a point mass at 0 (precipitation),
the CRPS is always computed by simulation,
with default |

`seed` |
Argument to |

`dates` |
The dates for which the CRPS will be computed.
These dates must be consistent with |

`...` |
Included for generic function compatibility. |

These methods are generic, and can be applied to all ensemble forecasting
models.

For `gamma0`

model for precipitation and the `gamma`

model
for wind speed the CRPS is only available through simulation.
The default number of simulation samples is 10,000.

Note that the `gamma0`

model for precipitation and the
`gamma`

model for wind speed may have been applied to a power
transformation of the data.

For normal models for temperature and pressure, analytic computation
of the CRPS is the default. CRPS will be computed via simulation for
normal models only if `nSamples`

is set to a positive value.

For the bivariate normal model for wind speed and direction, the
CRPS is computed for the marginal wind speed distribution.

`crps`

is a matrix giving the CRPS for each instance in the data
for both the raw ensemble and the median probabilistic forecast.

`CRPS`

is a vector giving the mean of the CRPS over all
instances for the raw ensemble and the median probabilistic forecast.

T. Gneiting and A. E. Raftery,
Strictly proper scoring rules, prediction and estimation,
*Journal of the American Statistical Association 102:359–378, 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. 516R, Department of Statistics, University of
Washington, 2007 (revised 2010).

`ensembleBMA`

,
`fitBMA`

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) # for quick run only; use more training days for forecasting tempTestFit <- ensembleBMAnormal( tempTestData[1:20,], trainingDays = 8) crpsValues <- crps( tempTestFit, tempTestData) colMeans(crpsValues) CRPS( tempTestFit, tempTestData)

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.