ensembleBMAgamma0 {ensembleBMA} | R Documentation |
Fits a Bayesian Model Averaging mixture of gammas with a point mass at 0 to ensemble forecasting data. Intended for modeling precipitation.
ensembleBMAgamma0( ensembleData, dates = NULL, trainingRule = list(length = 30, lag = 2), control = controlBMAgamma0(), warmStart = FALSE, popData = NULL)
ensembleData |
An ensembleData object including ensemble forecasts, observations
and dates of precipitation.
|
dates |
The dates for which modeling is 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 default is to use a 30 time step training period for a forecast
2 time steps ahead of the last time step in the training period.
|
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 initilaizatiin to be independent of the result at the previous time step. |
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.
|
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:
gridForecastBMA
, quantileForecastBMA
,
bmaModelParameters
, brierSkillScores
, and crpsANDmae
.
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 for each member of the ensemble at each date. |
biasCoefs |
The fitted coefficients in the model for the mean of nonzero observations for each member of the ensemble at each date (bias correction). |
varCoefs |
The fitted coefficients for the model for the variance of nonzero observations at each date. The coefficients are the same for all members of the ensemble. |
weights |
The fitted weights for the mixture of gammas model for the nonzero observations at each date. |
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.
|
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).
ensembleData
,
forecastBMAgamma0
,
controlBMAgamma0
,
fitBMAgamma0
,
gridForecastBMA
,
quantileForecastBMA
,
bmaModelParameters
,
brierSkillScores
,
crpsANDmae
,
## Not run: data(prcp) prcpData <- ensembleData( dates = prcp$date, observations = prcp$obs, forecasts = prcp[,c("CENT","AVN","CMCG","ETA", "GASP","JMA","NGPS","TCWB","UKMO")]) prcpFit <- ensembleBMAgamma0(prcpData) ## End(Not run)