afc-package               package:afc               R Documentation

_G_e_n_e_r_a_l_i_z_e_d _D_i_s_c_r_i_m_i_n_a_t_i_o_n _S_c_o_r_e _2_A_F_C

_D_e_s_c_r_i_p_t_i_o_n:

     This package is a collection of routines to calculate the
     "generalized discrimination score", which is also known as "two
     alternatives forced-choice score" or short: "2AFC-score". The 2AFC
     is a generic forecast verification framework which can be applied
     to any of the following verification contexts: dichotomous,
     polychotomous (ordinal and nominal), continuous, probabilistic,
     and ensemble. A comprehensive description of the 2AFC-score,
     including all equations used in this package, is provided by Mason
     and Weigel (2009).

     The master routine is 'afc'. For a given set of observation and
     forecast data, and for a specified verification context, 'afc'
     calls the appropriate functions which are necessary to calculate
     the 2AFC score. 

     Why the 2AFC-score? There are numerous reasons for calculating
     forecast verification scores, and considerable attention has been
     given to designing and analyzing the properties of scores that can
     be used for scientific purposes. Much less attention has been
     given to scores that may be useful for administrative reasons,
     such as communicating changes in forecast quality to bureaucrats,
     and providing indications of forecast quality to the general
     public. The 2AFC test a scoring procedure that is sufficiently
     generic to be useable on forecasts ranging from simply "yes"/"no"
     forecasts of dichotomous outcomes to continuous variables, and can
     be used with deterministic or probabilistic forecasts without
     seriously reducing the more complex information when available.
     Although, as with any single verification score, the 2AFC has
     limitations, it does have broad intuitive appeal in that the
     expected score of an unskilled set of forecasts (random guessing
     or perpetually identical forecasts) is 50%, and is interpretable
     as an indication of how often the forecasts are correct, even when
     the forecasts are expressed probabilistically and/or the
     observations are not discrete.

_D_e_t_a_i_l_s:


       Package:  afc
       Version:  1.03
       Date:     2010-01-07
       License:  GPL-2

     Index:


     afc                     Calculate Generalized Discrimination Score
     2AFC
     afc-package             Generalized Discrimination Score 2AFC
     afc.cc                  2AFC For Continuous Observations And
     Continuous
                             Forecasts
     afc.ce                  2AFC For Ordinal Polychotomous
     Observations And
                             Ensemble Forecasts
     afc.dc                  2AFC For Dichotomous Observations And
                             Continuous Forecasts
     afc.dd                  2AFC For Dichotomous Observations And
                             Dichotomous Forecasts
     afc.de                  2AFC for Dichotomous Observations and
     Ensemble
                             Forecasts
     afc.dm                  2AFC For Dichotomous Observations And
                             Polychotomous Forecasts
     afc.dp                  2AFC For Dichotomous Observations And
                             Probabilistic Forecasts
     afc.mc                  2AFC For Ordinal Polychotomous
     Observations And
                             Continuous Forecasts
     afc.me                  2AFC For Ordinal Polychotomous
     Observations And
                             Ensemble Forecasts
     afc.mm                  2AFC For Ordinal Polychotomous
     Observations And
                             Ordinal Polychotomous Forecasts
     afc.mp                  2AFC For Ordinal Polychotomous
     Observations And
                             Probabilistic Forecasts
     afc.nn                  2AFC For Nominal Polychotomous
     Observations And
                             Nominal Polychotomous Forecasts
     afc.np                  2AFC For Nominal Polychotomous
     Observations Ans
                             Probabilistic Forecasts
     cnrm.nino34.cc          Example Data of Continuous Observations
     and
                             Continuous Forecasts
     cnrm.nino34.ce          Example Data of Continuous Observations
     and
                             Ensemble Forecasts
     cnrm.nino34.dc          Example Data of Dichotomous Observations
     and
                             Continuous Forecasts
     cnrm.nino34.dd          Example Data of Dichotomous Observations
     and
                             Dichotomous Forecasts
     cnrm.nino34.de          Example Data of Dichotomous Observations
     and
                             Ensemble Forecasts
     cnrm.nino34.dm          Example Data of Dichotomous Observations
     and
                             Polychotomous Forecasts
     cnrm.nino34.dp          Example Data of Dichotomous Observations
     and
                             Polychotomous Forecasts
     cnrm.nino34.mc          Example Data of Polychotomous Observations
     and
                             Continuous Forecasts
     cnrm.nino34.me          Example Data of Polychotomous Observations
     and
                             Ensembles Forecasts
     cnrm.nino34.mm          Example Data of Polychotomous Observations
     and
                             Polychotomous Forecasts
     cnrm.nino34.mp          Example Data of Polychotomous Observations
     and
                             Probabilistic Forecasts
     rank.ensembles          Rank Ensembles


_A_u_t_h_o_r(_s):

     Andreas Weigel, Federal Office of Meteorology and Climatology
     (MeteoSwiss), Zurich, Switzerland <andreas.weigel@meteoswiss.ch>

_R_e_f_e_r_e_n_c_e_s:

     Mason, S.J. and A.P. Weigel, 2009: A generic forecast verification
     framework for administrative purposes. Mon. Wea. Rev., 137,
     331-349

