fitBMA | R Documentation |

Fits a Bayesian Modeling Averaging mixture model to a given training set.

fitBMA( ensembleData, control = NULL, model = NULL, exchangeable = NULL)

`ensembleData` |
An |

`control` |
A list of control values for the fitting functions.
The default is |

`model` |
A character string describing the BMA model to be fit.
Current choices are |

`exchangeable` |
A numeric or character vector or factor indicating groups of
ensemble members that are exchangeable (indistinguishable).
The model fit will have equal weights and parameters
within each group.
The default determines exchangeability from |

This function fits a BMA model to a training data set.

Methods available for `fitBMA`

objects (the output of `fitBMA`

)
include: `cdf`

, `quantileForecast`

, and
`modelParameters`

.

A list with the following output components:

`...` |
One or more components corresponding to the coeffcients of the model. |

`weights` |
The fitted BMA weights for the mixture components for each ensemble member. |

`nIter` |
The number of EM iterations. |

`power` |
A scalar value giving the power (if any) by which the data was transformed
for modeling.
The untransformed forecast is used to fit the variance model.
This is input as part of |

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. 516R, Department of Statistics, University of
Washington, 2007 (revised 2010).

C. Fraley, A. E. Raftery, T. Gneiting,
Calibrating Multi-Model Forecast Ensembles
with Exchangeable and Missing Members using Bayesian Model Averaging,
*Monthly Weather Review 138:190–202, 2010*.

J. M. Sloughter, T. Gneiting and A. E. Raftery,
Probabilistic wind speed forecasting
using ensembles and Bayesian model averaging,
*Journal of the American Statistical Association, 105:25–35, 2010*.

`ensembleData`

,
`ensembleBMA`

,
`fitBMAgamma`

,
`fitBMAgamma0`

,
`fitBMAnormal`

,
`cdf`

,
`quantileForecast`

,
`modelParameters`

,
`controlBMAgamma`

,
`controlBMAgamma0`

,
`controlBMAnormal`

data(ensBMAtest) ensNames <- c("gfs","cmcg","eta","gasp","jma","ngps","tcwb","ukmo") obs <- paste("T2","obs", sep = ".") ens <- paste("T2", ensNames, sep = ".") tempTestData <- ensembleData( forecasts = ensBMAtest[,ens], observations = ensBMAtest[,obs], station = ensBMAtest[,"station"], dates = ensBMAtest[,"vdate"], forecastHour = 48, initializationTime = "00") tempTrain <- trainingData( tempTestData, trainingDays = 30, date = "2008010100") tempTrainFit <- fitBMA( tempTrain, model = "normal") ## equivalent to ## tempTrainFit <- fitBMAnormal( tempTrain) set.seed(0); exch <- sample(1:length(ens),replace=TRUE) tempTestData <- ensembleData( forecasts = ensBMAtest[,ens], exchangeable = exch, observations = ensBMAtest[,obs], station = ensBMAtest[,"station"], dates = ensBMAtest[,"vdate"], forecastHour = 48, initializationTime = "00")

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