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#' 2AFC For Dichotomous Observations And Probabilistic Forecasts
#'
#' Routine to calculate the Generalized Discrimination Score (aka
#' Two-Alternatives Forced Choice Score 2AFC) for the situation of dichotomous
#' observations and probabilistic forecasts
#'
#' This routine applies Eq.3 of Mason and Weigel (2009) to calculate the 2AFC.
#'
#' @param obsv vector with dichotomous observations (values in \{0,1\})
#' @param fcst vector of same length as \emph{obsv} with forecast probabilities
#' for the event to happen
#' @param na.rm logical; if \code{TRUE} pairs where \code{obsv} or
#' \code{fcst} is \code{NA} are removed before scoring. Default
#' \code{FALSE} returns \code{NA} if any input contains \code{NA}.
#' @return \item{ p.afc }{ Value of Generalized Discrimination (2AFC) Score }
#' @author Andreas Weigel, Federal Office of Meteorology and Climatology,
#' MeteoSwiss, Zurich, Switzerland
#' @seealso \code{\link{afc}}
#' @references S.J. Mason and A.P. Weigel, 2009. A generic verification
#' framework for administrative purposes. Mon. Wea. Rev., 137, 331-349
#' @keywords file
#' @examples
#' data(cnrm.nino34.dp)
#' obsv = cnrm.nino34.dp$obsv
#' fcst = cnrm.nino34.dp$fcst
#' afc.dp(obsv,fcst)
#' @export afc.dp
afc.dp = function(obsv, fcst, na.rm = FALSE) {
if (na.rm) {
d <- .complete_cases(obsv, fcst); obsv <- d$obsv; fcst <- d$fcst
} else if (anyNA(obsv) || anyNA(fcst)) {
return(NA_real_)
}
n1 = sum(obsv)
n0 = length(obsv)-sum(obsv)
fcst.1 = fcst[which(obsv == 1)]
fcst.0 = fcst[which(obsv == 0)]
probs = sort(unique(fcst))
mf = length(probs)
n0.mf = array(NA,mf)
n1.mf = array(NA,mf)
for (i in 1:mf){
n0.mf[i] = length(which(fcst.0 == probs[i]))
n1.mf[i] = length(which(fcst.1 == probs[i]))
}
summand1 = 0
summand2 = 0
for (i in 1:(mf-1)) for (j in (i+1):mf)
summand1 = summand1 + n0.mf[i]*n1.mf[j]
for (k in 1:mf)
summand2 = summand2 + n0.mf[k]*n1.mf[k]
p.afc = (summand1 + 0.5*summand2)/(n0*n1)
type.flag = 1
return(p.afc)
}
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