afc: Calculate Generalized Discrimination Score 2AFC
In afc: Generalized Discrimination Score
afc R Documentation
Calculate Generalized Discrimination Score 2AFC
Description
This is the master routine for the calculation the Generalized
Discrimination Score (aka Two-Alternatives Forced Choice Score - 2AFC) as
described in the Paper of Mason and Weigel (2009). This routine requires, as
input, datasets of forecasts and corresponding observations, as well as a
specification of the verification context. The routine checks whether the
input data are consistent with the verification context, then calls the
appropriate function to calculate the 2AFC, and finally returns the 2AFC
skill value.
Usage
afc(obsv, fcst, obsv.type, fcst.type, m = 0, m2 = 0, na.rm = FALSE)
Arguments
obsv
Vector of observations. The required format depends on the
specific verification context. More details below.
fcst
Vector or two-dimensional array of forecasts. The required
format depends on the specific verification context. More details below.
obsv.type
Character specifying the type of the observations. Possible
values: "d" (dichotomous), "m" (polychotomous with ordinal categories), "n"
(polychotomous with nominal categories), "c" (continuous).
fcst.type
Character specifying the type of the forecasts. Possible
values: "d" (dichotomous), "m" (polychotomous with ordinal categories), "n"
(polychotomous with nominal categories), "p" (probabilistic), "c"
(continuous) and "e" (ensemble).
m
Number of observation or forecast categories (only required if
obsv.type or fcst.type equals "m" or "n")
m2
Number of forecast categories (only required if both obsv.type and
fcst.type equal "m"). The number of observation categories is then specified
by the argument m above.
na.rm
logical; if TRUE pairs where obsv or
fcst is NA are removed before scoring. Default
FALSE returns NA if any input contains NA.
Details
Depending on the specific verification context (i.e. the choice for
obsv.type and fcst.type), this routine calls the appropriate
function(s) to calculate the 2AFC score. The following combinations of
obsv.type and fcst.type are possible: (1) "d-d"; (2) "d-m";
(3) "d-p"; (4) "d-c"; (5) "d-e"; (6) "m-m"; (7) "m-p"; (8) "m-c"; (9) "m-e";
(10) "n-n"; (11) "n-p"; (12) "c-c"; (13) "c-e". The required format of the
input data obsv and fcst depends on the verification
context:
(1) "d-d":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: vector of same length as obsv with dichotomous
forecasts (values in {0,1})
(2) "d-m":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: vector of same length as obsv with
polychotomous forecasts (values in {1,..,m})
(3) "d-p":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: vector of same length as obsv with forecast
probabilities for the event to happen
(4) "d-c":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: vector of same length as obsv with real-valued
forecasts
(5) "d-e":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: two-dimensional array with ensemble forecasts;
dim(fcst)[1] = length(obsv); dim(fcst)[2] = ensemble size.
(6) "m-m":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: vector of same length as obsv with
polychotomous forecasts (values in {1,..,m2})
(7) "m-p":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: two-dimensional array with forecast
probabilities for the m categories; dim(fcst)[1] = length(obsv);
dim(fcst)[2] = m
(8) "m-c":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: vector of same length as obsv with
real-valued forecasts
(9) "m-e":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: two-dimensional array with ensemble forecasts;
dim(fcst)[1] = length(obsv); dim(fcst)[2] = ensemble size.
(10) "n-n":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: vector of same length as obsv with
polychotomous forecasts (values in {1,..,m})
(11) "n-p":
Same as "m-p".
(12) "c-c":
obsv: vector with real-valued observations
fcst: vector of same length as obsv with real-valued forecasts
(13) "c-e":
obsv: vector with real-valued observations
fcst: two-dimensional array with ensemble forecasts; dim(fcst)[1] =
length(obsv); dim(fcst)[2] = ensemble size.
Value
p.afc
Value of Generalized Discrimination Score (2AFC)
Author(s)
Andreas Weigel, Federal Office of Meteorology and Climatology,
MeteoSwiss, Zurich, Switzerland
References
S.J. Mason and A.P. Weigel, 2009. A generic verification
framework for administrative purposes. Mon. Wea. Rev., 137, 331-349
See Also
afc.dd afc.dm afc.dp
afc.dc afc.de afc.mm
afc.mp afc.mc afc.me
afc.nn afc.np afc.cc
afc.ce
Examples
data(cnrm.nino34.dd)
obsv = cnrm.nino34.dd$obsv
fcst = cnrm.nino34.dd$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="d")
data(cnrm.nino34.dm)
obsv = cnrm.nino34.dm$obsv
fcst = cnrm.nino34.dm$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="m", m=4)
data(cnrm.nino34.dp)
obsv = cnrm.nino34.dp$obsv
fcst = cnrm.nino34.dp$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="p")
data(cnrm.nino34.dc)
obsv = cnrm.nino34.dc$obsv
fcst = cnrm.nino34.dc$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="c")
data(cnrm.nino34.de)
obsv = cnrm.nino34.de$obsv
fcst = cnrm.nino34.de$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="e")
data(cnrm.nino34.mm)
obsv = cnrm.nino34.mm$obsv
fcst = cnrm.nino34.mm$fcst
afc(obsv, fcst, obsv.type="m", fcst.type="m", m=4, m2=4)
data(cnrm.nino34.mp)
obsv = cnrm.nino34.mp$obsv
fcst = cnrm.nino34.mp$fcst
afc(obsv, fcst, obsv.type="m", fcst.type="p", m=4)
data(cnrm.nino34.mc)
obsv = cnrm.nino34.mc$obsv
fcst = cnrm.nino34.mc$fcst
afc(obsv, fcst, obsv.type="m", fcst.type="c", m=4)
data(cnrm.nino34.me)
obsv = cnrm.nino34.me$obsv
fcst = cnrm.nino34.me$fcst
afc(obsv, fcst, obsv.type="m", fcst.type="e", m=4)
data(cnrm.nino34.mm)
obsv = cnrm.nino34.mm$obsv
fcst = cnrm.nino34.mm$fcst
afc(obsv, fcst, obsv.type="n", fcst.type="n", m=4)
data(cnrm.nino34.mp)
obsv = cnrm.nino34.mp$obsv
fcst = cnrm.nino34.mp$fcst
afc(obsv, fcst, obsv.type="n", fcst.type="p", m=4)
data(cnrm.nino34.cc)
obsv = cnrm.nino34.cc$obsv
fcst = cnrm.nino34.cc$fcst
afc(obsv, fcst, obsv.type="c", fcst.type="c")
data(cnrm.nino34.ce)
obsv = cnrm.nino34.ce$obsv
fcst = cnrm.nino34.ce$fcst
afc(obsv, fcst, obsv.type="c", fcst.type="e")
afc documentation built on Sept. 11, 2026, 9:08 a.m.
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| afc | R Documentation |
Calculate Generalized Discrimination Score 2AFC
Description
This is the master routine for the calculation the Generalized Discrimination Score (aka Two-Alternatives Forced Choice Score - 2AFC) as described in the Paper of Mason and Weigel (2009). This routine requires, as input, datasets of forecasts and corresponding observations, as well as a specification of the verification context. The routine checks whether the input data are consistent with the verification context, then calls the appropriate function to calculate the 2AFC, and finally returns the 2AFC skill value.
Usage
afc(obsv, fcst, obsv.type, fcst.type, m = 0, m2 = 0, na.rm = FALSE)
Arguments
obsv |
Vector of observations. The required format depends on the specific verification context. More details below. |
fcst |
Vector or two-dimensional array of forecasts. The required format depends on the specific verification context. More details below. |
obsv.type |
Character specifying the type of the observations. Possible values: "d" (dichotomous), "m" (polychotomous with ordinal categories), "n" (polychotomous with nominal categories), "c" (continuous). |
fcst.type |
Character specifying the type of the forecasts. Possible values: "d" (dichotomous), "m" (polychotomous with ordinal categories), "n" (polychotomous with nominal categories), "p" (probabilistic), "c" (continuous) and "e" (ensemble). |
m |
Number of observation or forecast categories (only required if obsv.type or fcst.type equals "m" or "n") |
m2 |
Number of forecast categories (only required if both obsv.type and fcst.type equal "m"). The number of observation categories is then specified by the argument m above. |
na.rm |
logical; if |
Details
Depending on the specific verification context (i.e. the choice for
obsv.type and fcst.type), this routine calls the appropriate
function(s) to calculate the 2AFC score. The following combinations of
obsv.type and fcst.type are possible: (1) "d-d"; (2) "d-m";
(3) "d-p"; (4) "d-c"; (5) "d-e"; (6) "m-m"; (7) "m-p"; (8) "m-c"; (9) "m-e";
(10) "n-n"; (11) "n-p"; (12) "c-c"; (13) "c-e". The required format of the
input data obsv and fcst depends on the verification
context:
(1) "d-d":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: vector of same length as obsv with dichotomous
forecasts (values in {0,1})
(2) "d-m":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: vector of same length as obsv with
polychotomous forecasts (values in {1,..,m})
(3) "d-p":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: vector of same length as obsv with forecast
probabilities for the event to happen
(4) "d-c":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: vector of same length as obsv with real-valued
forecasts
(5) "d-e":
obsv: vector with dichotomous observations (values in
{0,1})
fcst: two-dimensional array with ensemble forecasts;
dim(fcst)[1] = length(obsv); dim(fcst)[2] = ensemble size.
(6) "m-m":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: vector of same length as obsv with
polychotomous forecasts (values in {1,..,m2})
(7) "m-p":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: two-dimensional array with forecast
probabilities for the m categories; dim(fcst)[1] = length(obsv);
dim(fcst)[2] = m
(8) "m-c":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: vector of same length as obsv with
real-valued forecasts
(9) "m-e":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: two-dimensional array with ensemble forecasts;
dim(fcst)[1] = length(obsv); dim(fcst)[2] = ensemble size.
(10) "n-n":
obsv: vector with polychotomous observations (values
in {1,..,m})
fcst: vector of same length as obsv with
polychotomous forecasts (values in {1,..,m})
(11) "n-p":
Same as "m-p".
(12) "c-c":
obsv: vector with real-valued observations
fcst: vector of same length as obsv with real-valued forecasts
(13) "c-e":
obsv: vector with real-valued observations
fcst: two-dimensional array with ensemble forecasts; dim(fcst)[1] =
length(obsv); dim(fcst)[2] = ensemble size.
Value
p.afc |
Value of Generalized Discrimination Score (2AFC) |
Author(s)
Andreas Weigel, Federal Office of Meteorology and Climatology, MeteoSwiss, Zurich, Switzerland
References
S.J. Mason and A.P. Weigel, 2009. A generic verification framework for administrative purposes. Mon. Wea. Rev., 137, 331-349
See Also
afc.dd afc.dm afc.dp
afc.dc afc.de afc.mm
afc.mp afc.mc afc.me
afc.nn afc.np afc.cc
afc.ce
Examples
data(cnrm.nino34.dd)
obsv = cnrm.nino34.dd$obsv
fcst = cnrm.nino34.dd$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="d")
data(cnrm.nino34.dm)
obsv = cnrm.nino34.dm$obsv
fcst = cnrm.nino34.dm$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="m", m=4)
data(cnrm.nino34.dp)
obsv = cnrm.nino34.dp$obsv
fcst = cnrm.nino34.dp$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="p")
data(cnrm.nino34.dc)
obsv = cnrm.nino34.dc$obsv
fcst = cnrm.nino34.dc$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="c")
data(cnrm.nino34.de)
obsv = cnrm.nino34.de$obsv
fcst = cnrm.nino34.de$fcst
afc(obsv, fcst, obsv.type="d", fcst.type="e")
data(cnrm.nino34.mm)
obsv = cnrm.nino34.mm$obsv
fcst = cnrm.nino34.mm$fcst
afc(obsv, fcst, obsv.type="m", fcst.type="m", m=4, m2=4)
data(cnrm.nino34.mp)
obsv = cnrm.nino34.mp$obsv
fcst = cnrm.nino34.mp$fcst
afc(obsv, fcst, obsv.type="m", fcst.type="p", m=4)
data(cnrm.nino34.mc)
obsv = cnrm.nino34.mc$obsv
fcst = cnrm.nino34.mc$fcst
afc(obsv, fcst, obsv.type="m", fcst.type="c", m=4)
data(cnrm.nino34.me)
obsv = cnrm.nino34.me$obsv
fcst = cnrm.nino34.me$fcst
afc(obsv, fcst, obsv.type="m", fcst.type="e", m=4)
data(cnrm.nino34.mm)
obsv = cnrm.nino34.mm$obsv
fcst = cnrm.nino34.mm$fcst
afc(obsv, fcst, obsv.type="n", fcst.type="n", m=4)
data(cnrm.nino34.mp)
obsv = cnrm.nino34.mp$obsv
fcst = cnrm.nino34.mp$fcst
afc(obsv, fcst, obsv.type="n", fcst.type="p", m=4)
data(cnrm.nino34.cc)
obsv = cnrm.nino34.cc$obsv
fcst = cnrm.nino34.cc$fcst
afc(obsv, fcst, obsv.type="c", fcst.type="c")
data(cnrm.nino34.ce)
obsv = cnrm.nino34.ce$obsv
fcst = cnrm.nino34.ce$fcst
afc(obsv, fcst, obsv.type="c", fcst.type="e")
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