Computes the median, 10th and 90th percentile forecasts, and plots the corresponding observations.
1  verifPlot( fit, ensembleData, dates = NULL)

fit 
A model fit to ensemble forecasting data. 
ensembleData 
An 
dates 
The dates for which the CDF will be computed.
These dates must be consistent with 
A matrix giving the median, 10th and 90th percentile forecasts for the ensemble data at the specified dates. If observations are available, they are plotted along with the forecasts in order of increasing 90th percentile forecast.
A. E. Raftery, T. Gneiting, F. Balabdaoui and M. Polakowski, Using Bayesian model averaging to calibrate forecast ensembles, Monthly Weather Review 133:11551174, 2005.
T. Gneiting, F. Balabdaoui and A. Raftery, Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society, Series B 69:243–268, 2007.
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 Ensemble 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 MultiModel 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.
1 2 3 4  data(prcpFit)
data(prcpDJdata)
forc < verifPlot( prcpFit, prcpDJdata, date = "20030113")

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