fitBMAgamma0 {ensembleBMA}R Documentation

BMA for a mixture of gammas with a point mass at 0.

Description

Fits a Bayesian Modeling Averaging mixture of gammas with a point mass at 0 to ensemble forecasting data. Intended for modeling precipitation.

Usage

fitBMAgamma0( ensembleData, control = controlBMAgamma0(), popData = NULL) 

Arguments

ensembleData An ensembleData object with forecasts, observations and dates for precipitation.
control A list of control values for the fitting functions. The defaults are given by the function controlBMAgamma0.
popData Optional predictors for the logistic regression for probability of precipitation. This option applies to the mixture of gammas model with a point mass at zero that is used for precipitation. In this model, the default predictors are an intercept, the transformed forecast data, and a logical variable indicating whether or not the forecast is equal to 0. In addition, the coefficient of the transformed forecast must not be greater than 0 and that of the logical variable must not be less than 0 in the default. If provided, the predictors in popData would replace the logical variable in the regression, and no constraints are imposed on the regression coefficients.
To supply one pop predictor per ensemble member, popData can be a matrix or data frame (if the predictor is categorical) with number of rows equal to the number of observations, and number of columns equal to the ensemble size.
To supply multiple numeric pop predictors per ensemble member, popData can be an array of dimension (number of observations) by (ensemble size) by (number of pop predictors).
To supply multiple pop predictors per ensemble member, some of which may be categorical, popData must be a list of (number of observations) by (ensemble size) matrices, one for each pop predictor.

Details

This function fits a BMA model to a training data set.
It is called by ensembleBMAgamma0, which produces a sequence of fits over a larger precipitation data set.
The following methods are available for the output of fitBMA: gridForecastBMA, quantileForecastBMA, and bmaModelParameters.

Value

A list with the following output components:

prob0coefs The fitted coefficients in the model for the point mass at 0 for each member of the ensemble.
biasCoefs The fitted coefficients in the model for the mean of nonzero observations for each member of the ensemble (used for bias correction).
varCoefs The fitted coefficients for the model for the variance of nonzero observations (these are the same for all members of the ensemble).
weights The fitted weights for the mixture of gammas model for the nonzero observations.
nIter The number of EM iterations.
transformation The function corresponding the transformation of the data used to fit the models for the point mass at 0 and mean of nonzero observations. 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 in various diagnostic methods for the output.

References

J. M. Sloughter, A. E. Raftery, T. Gneiting and C. Fraley, Probabilistic Quantitative Precipitation Forecasting using Bayesian Model Averaging, Technical Report No. 496R, Department of Statistics, University of Washington, October 2006 (to appear in Montly Weather Review).

See Also

ensembleData, controlBMAgamma0, ensembleBMAgamma0, gridForecastBMA, quantileForecastBMA, bmaModelParameters

Examples

  data(prcp)

  prcpData <- ensembleData( dates = prcp$date, observations = prcp$obs,
                          forecasts = prcp[,c("CENT","AVN","CMCG","ETA",
                                      "GASP","JMA","NGPS","TCWB","UKMO")])

  DATE <- sort(unique(prcpData$dates))[27]
  trainDat <- trainingData(prcpData, date = DATE,
                           trainingRule = list(length=25,lag=2))
  prcpFit25a <- fitBMAgamma0(trainDat)

  D <- as.numeric(prcpData$dates) <= 25
  prcpFit25b <- fitBMAgamma0(prcpData[D, ])

  prcpFit25c <- fitBMAgamma0(prcpData[D, ], 
                     popData = ensembleForecasts(prcpData[D,]) == 0)

[Package ensembleBMA version 2.0 Index]