Nothing
cdf.ensembleMOSgev0 <-
function(fit, ensembleData, values, dates = NULL, randomizeATzero = FALSE, ...)
{
gini.md <- function(x,na.rm=FALSE) { ## Michael Scheuerer's code
if(na.rm & any(is.na(x))) x <- x[!is.na(x)]
n <-length(x)
return(4*sum((1:n)*sort(x,na.last=TRUE))/(n^2)-2*mean(x)*(n+1)/n)
}
matchITandFH(fit,ensembleData)
M <- matchEnsembleMembers(fit,ensembleData)
nForecasts <- ensembleSize(ensembleData)
if (!all(M == 1:nForecasts)) ensembleData <- ensembleData[,M]
## remove instances missing all forecasts
M <- apply(ensembleForecasts(ensembleData), 1, function(z) all(is.na(z)))
ensembleData <- ensembleData[!M,]
## match specified dates with dateTable in fit
dateTable <- dimnames(fit$B)[[2]]
if (!is.null(dates)) {
dates <- sort(unique(as.character(dates)))
if (length(dates) > length(dateTable))
stop("parameters not available for some dates")
K <- match( dates, dateTable, nomatch=0)
if (any(!K) || !length(K))
stop("parameters not available for some dates")
}
else {
dates <- dateTable
K <- 1:length(dateTable)
}
ensDates <- ensembleValidDates(ensembleData)
## match dates in data with dateTable
if (is.null(ensDates) || all(is.na(ensDates))) {
if (length(dates) > 1) stop("date ambiguity")
nObs <- nrow(ensembleData)
Dates <- rep( dates, nObs)
}
else {
## remove instances missing dates
if (any(M <- is.na(ensDates))) {
ensembleData <- ensembleData[!M,]
ensDates <- ensembleValidDates(ensembleData)
}
Dates <- as.character(ensDates)
L <- as.logical(match( Dates, dates, nomatch=0))
if (all(!L) || !length(L))
stop("model fit dates incompatible with ensemble data")
Dates <- Dates[L]
ensembleData <- ensembleData[L,]
nObs <- length(Dates)
}
nForecasts <- ensembleSize(ensembleData)
nVal <- length(values)
CDF <- matrix(NA, nObs, nVal)
dimnames(CDF) <- list(ensembleObsLabels(ensembleData),
as.character(values))
ensembleData <- ensembleForecasts(ensembleData)
l <- 0
for (d in dates) {
l <- l + 1
k <- K[l]
B <- fit$B[,k]
if (all(Bmiss <- is.na(B))) next
A <- fit$a[,k]
C <- fit$c[,k]
D <- fit$d[,k]
SHAPE <- fit$q[,k]
S <- fit$s[,k]
I <- which(as.logical(match(Dates, d, nomatch = 0)))
for (i in I) {
f <- ensembleData[i,]
MEAN <- as.numeric(c(A,B)%*%c(1,f)+S*mean(f==0, na.rm = TRUE)) #location of GEV
SCALE <- C + D*gini.md(f, na.rm = TRUE) #scale of GEV
LOC <- as.numeric(MEAN - SCALE*(gamma(1-SHAPE)-1)/SHAPE)
if (randomizeATzero){
cdfval <- pgev(values, loc=LOC, scale=SCALE, shape=SHAPE)
zeroval <- runif(nVal,min=rep(0,nVal),max=cdfval)
CDF[i,] <- cdfval * (values>0)+ zeroval * (values==0)
} else {
CDF[i,] <- pgev(values, loc=LOC, scale=SCALE, shape=SHAPE)
}
}
}
if (any(is.na(CDF))) warning("NAs in cdf values")
CDF
}
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