Nothing
#' 2AFC For Nominal Polychotomous Observations And Nominal Polychotomous
#' Forecasts
#'
#' Routine to calculate the Generalized Discrimination Score (aka
#' Two-Alternatives Forced Choice Score 2AFC) for the situation of
#' polychotomous observations (nominal) and polychotomous forecasts (nominal)
#'
#' This routine applies Eq.15 of Mason and Weigel (2009) to calculate the 2AFC.
#'
#' @param obsv vector with polychotomous observations (values in {1,..,m})
#' @param fcst vector of same length as \emph{obsv} with polychotomous
#' forecasts (values in {1,..,m})
#' @param m number of observation and forecast categories (default = 3)
#' @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
#'
#' #Forecasts and observations of Nino-3.4 index
#' #Load set of polychotomous observations and polychotomous forecasts (4 categories)
#' data(cnrm.nino34.mm)
#' obsv = cnrm.nino34.mm$obsv
#' fcst = cnrm.nino34.mm$fcst
#'
#' #Calculate skill score
#' afc.nn(obsv,fcst,4)
#'
#' @export afc.nn
afc.nn = function(obsv,fcst,m=3){
#################################
# OBSV: POLYCHOTOMOUS (NOMINAL) #
# FCST: POLYCHOTOMOUS (NOMINAL) #
#################################
# input variables:
# ----------------
# fcst - vector with forecast categories (1...m)
# obsv - vector with observation categories (1...m)
# m - Number of forecast categories (default = 3)
#
# Here it is assumed that both observations and forecasts
# have the same number of categories
# output variable:
# ----------------
# p.afc - 2AFC skill score as obtained from MW09 Eq. 15 (corrigendum)
# Matrix with element[nn,mm] being the number of times
# that nn has been observed and mm has been forecast
n.matrix = array(0,dim=c(m,m))
for (nn in 1:m) for (mm in 1:m){
n.matrix[nn,mm] = sum((obsv == nn) & (fcst == mm))
}
# Solve Eq. 15 of corrigendum of MW09
numer = 0
denom = 0
for (k in 1:m) for (l in (1:m)[-k]){
term1 = 0
term2 = 0
term3 = 0
for (i in (1:m)[-k]) term1 = term1 + n.matrix[k,k]*n.matrix[l,i]
for (i in (1:m)[-k]) for (j in (1:m)[-c(k,l)])
term2 = term2 + n.matrix[k,l]*n.matrix[i,j]
for (i in 1:m) term3 = term3 + n.matrix[k,i]*n.matrix[l,i]
numer = numer + term1 + 0.5*term2 + 0.5*term3
denom = denom + sum(n.matrix[k,])*sum(n.matrix[l,])
}
p.afc = numer/denom
type.flag = 1
return(p.afc)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.