ensembleBMAgamma0 {ensembleBMA}R Documentation

BMA precipitation modeling

Description

Fits a Bayesian Model Averaging mixture of gammas with a point mass at 0 to ensemble forecasts. Intended for predicting precipitation. Allows specification of a training rule and forecasting dates.

Usage

ensembleBMAgamma0( ensembleData, dates = NULL, 
                   trainingRule = list(length = NA, lag = NA), 
                   control = controlBMAgamma0(), warmStart = FALSE, 
                   exchangeable = NULL)

Arguments

ensembleData An ensembleData object including ensemble forecasts, verification observations and dates. Missing values (indicated by NA) are allowed.
dates The dates for which forecasting models are desired. By default, this will include all dates consistent with the training rule.
trainingRule A list giving the length and lag for the training period. The length gives the number of time steps (e.g. days) in the training period, and the lag gives the number of time steps ahead of the most recent date in the training period for which the forecast is valid. There is no default.
control A list of control values for the fitting functions. The defaults are given by the function controlBMAgamma0.
warmStart A logical variable indicating whether or not estimation of models for a sequence of dates or time steps should be initialized with the weights from the previous date or time step. The default is for the initialization to be independent of the result at the previous time step.
exchangeable A numeric or character vector or factor indicating groups of ensemble members that are exchangeable (indistinguishable). The models fit will have equal weights and parameters within each group. The default determines exchangeability from ensembleData.

Details

The output is for all of the dates in ensembleBMA, so there will be missing entries denoted by NA for dates that are too recent to be forecast with the training rule.
The following methods are available for ensembleBMAgamma0 objects: cdf, quantileForecast, modelParameters, brierScore, crps and mae.

Value

A list with the following output components:

dateTable The table of observations corresponding to the dates in ensembleData in chronological order.
trainingRule The training rule specified as input.
prob0coefs The fitted coefficients in the model for the point mass at 0 (probability of zero precipitaion) for each member of the ensemble at each date.
biasCoefs The fitted coefficients in the model for the mean of the gamma components for each member of the ensemble at each date (bias correction).
varCoefs The fitted coefficients for the model for the variance of gamma components for each date. The coefficients are the same for all members of the ensemble.
weights The fitted BMA weights for the gamma components for each ensemble member at each date.
transformation The function corresponding to the transformation of the data used to fit the models for the point mass at 0 and the bias model. The untransformed forecast is used to fit the variance model. This is input as part of control.
inverseTransformation The function corresponding to the inverse of transformation. Used for quantile forecasts and verification. This is input as part of control.

References

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, May 2008.

See Also

ensembleData, controlBMAgamma0, trainingControl, fitBMAgamma0, cdf, quantileForecast, modelParameters, brierScore, crps, mae

Examples

## Not run: 
  data(prcpTest)

  labels <- c("CENT","AVN","CMCG","ETA","GASP","JMA","NGPS","TCWB","UKMO")

  prcpTestData <- ensembleData( forecasts = prcpTest[ ,labels],
                          dates = prcpTest$date, observations = prcpTest$obs)

## Not run: 
  prcpTestFit <- ensembleBMA( prcpTestData, model = "gamma0",
                              trainingRule = list(length=30,lag=2))
## End(Not run)
  prcpTestFit <- ensembleBMAgamma0( prcpTestData,
                              trainingRule = list(length=30,lag=2))
## End(Not run)

[Package ensembleBMA version 3.0-2 Index]