ensembleBMAgamma0 {ensembleBMA} | R Documentation |
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.
ensembleBMAgamma0( ensembleData, dates = NULL, trainingRule = list(length = NA, lag = NA), control = controlBMAgamma0(), warmStart = FALSE, exchangeable = NULL)
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 .
|
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
.
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 .
|
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.
ensembleData
,
controlBMAgamma0
,
trainingControl
,
fitBMAgamma0
,
cdf
,
quantileForecast
,
modelParameters
,
brierScore
,
crps
,
mae
## 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)