afc {afc} | R Documentation |
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.
afc(obsv, fcst, obsv.type, fcst.type, m = 0, m2 = 0)
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. |
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.
p.afc |
Value of Generalized Discrimination Score (2AFC) |
Andreas Weigel, Federal Office of Meteorology and Climatology, MeteoSwiss, Zurich, Switzerland
S.J. Mason and A.P. Weigel, 2009. A generic verification framework for administrative purposes. Mon. Wea. Rev., 137, 331-349
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
# #In all following examples, forecasts of the Nino-3.4 index are evaluated # #---------------------- #Example 1: Dichotomous observations, dichotomous forecasts # --------------------- #Load set of dichotomous observations and dichotomous forecasts data(cnrm.nino34.dd) obsv = cnrm.nino34.dd$obsv fcst = cnrm.nino34.dd$fcst #Calculate skill score afc(obsv, fcst, obsv.type="d", fcst.type="d") # --------------------- #Example 2: Dichotomous observations, (ordinal) polychotomous forecasts # --------------------- #Load set of dichotomous observations and polychotomous forecasts (4 categories) data(cnrm.nino34.dm) obsv = cnrm.nino34.dm$obsv fcst = cnrm.nino34.dm$fcst #Calculate skill score afc(obsv, fcst, obsv.type="d", fcst.type="m", m=4) # --------------------- #Example 3: Dichotomous observations, probabilistic forecasts # --------------------- #Load set of dichotomous observations and probabilistic forecasts data(cnrm.nino34.dp) obsv = cnrm.nino34.dp$obsv fcst = cnrm.nino34.dp$fcst #Calculate skill score afc(obsv, fcst, obsv.type="d", fcst.type="p") # --------------------- #Example 4: Dichotomous observations, continuous forecasts # --------------------- #Load set of dichotomous observations and continuous forecasts data(cnrm.nino34.dc) obsv = cnrm.nino34.dc$obsv fcst = cnrm.nino34.dc$fcst #Calculate skill score afc(obsv, fcst, obsv.type="d", fcst.type="c") # --------------------- #Example 5: Dichotomous observations, ensemble forecasts # --------------------- #Load set of dichotomous observations and 9-member ensemble forecasts data(cnrm.nino34.de) obsv = cnrm.nino34.de$obsv fcst = cnrm.nino34.de$fcst #Calculate skill score afc(obsv, fcst, obsv.type="d", fcst.type="e") # --------------------- #Example 6: Polychotomous (ordinal) observations, polychotomous (ordinal) forecasts # --------------------- #Load set of polychotomous observations (4 categories) and polychotomous forecasts (4 categories) data(cnrm.nino34.mm) obsv = cnrm.nino34.mm$obsv fcst = cnrm.nino34.mm$fcst #Calculate skill score afc(obsv, fcst, obsv.type="m", fcst.type="m", m=4, m2=4) # --------------------- #Example 7: Polychotomous (ordinal) observations, probabilistic forecasts forecasts # --------------------- #Load set of polychotomous observations (4 categories) and probabilistic forecasts data(cnrm.nino34.mp) obsv = cnrm.nino34.mp$obsv fcst = cnrm.nino34.mp$fcst #Calculate skill score afc(obsv, fcst, obsv.type="m", fcst.type="p", m=4) # --------------------- #Example 8: Polychotomous (ordinal) observations, continuous forecasts # --------------------- #Load set of polychotomous observations (4 categories) and continuous forecasts data(cnrm.nino34.mc) obsv = cnrm.nino34.mc$obsv fcst = cnrm.nino34.mc$fcst #Calculate skill score afc(obsv, fcst, obsv.type="m", fcst.type="c", m=4) # --------------------- #Example 9: Polychotomous (ordinal) observations, ensemble forecasts # --------------------- #Load set of polychotomous observations (4 categories) and 9-member ensemble forecasts data(cnrm.nino34.me) obsv = cnrm.nino34.me$obsv fcst = cnrm.nino34.me$fcst #Calculate skill score afc(obsv, fcst, obsv.type="m", fcst.type="e", m=4) # --------------------- #Example 10: Polychotomous (nominal) observations, polychotomous (nominal) forecasts # --------------------- #Load set of polychotomous observations (4 categories) and polychotomous forecasts (4 categories) data(cnrm.nino34.mm) obsv = cnrm.nino34.mm$obsv fcst = cnrm.nino34.mm$fcst #Calculate skill score afc(obsv, fcst, obsv.type="n", fcst.type="n", m=4) # --------------------- #Example 11: Polychotomous (nominal) observations, probabilistic forecasts # --------------------- #Load set of polychotomous observations (4 categories) and probabilistic forecasts data(cnrm.nino34.mp) obsv = cnrm.nino34.mp$obsv fcst = cnrm.nino34.mp$fcst #Calculate skill score afc(obsv, fcst, obsv.type="n", fcst.type="p", m=4) # --------------------- #Example 12: Continuous observations, continuous forecasts # --------------------- #Load set of continuous observations and continuous forecasts data(cnrm.nino34.cc) obsv = cnrm.nino34.cc$obsv fcst = cnrm.nino34.cc$fcst #Calculate skill score afc(obsv, fcst, obsv.type="c", fcst.type="c") # --------------------- #Example 13: Continuous observations, ensemble forecasts # --------------------- #Load set of continuous observations and 9-member ensemble forecasts data(cnrm.nino34.ce) obsv = cnrm.nino34.ce$obsv fcst = cnrm.nino34.ce$fcst #Calculate skill score afc(obsv, fcst, obsv.type="c", fcst.type="e")