Nothing
quantileForecast.ensembleMOScsg0 <-
function(fit, ensembleData, quantiles = 0.5, dates = NULL, ...)
{
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$B)[[2]]
if (!is.null(dates)) {
dates <- sort(unique(as.character(dates)))
if (length(dates) > length(dateTable))
stop("parameters not available for some dates")
K <- match( dates, dateTable, nomatch=0)
if (any(!K) || !length(K))
stop("parameters not available for some dates")
}
else {
dates <- dateTable
K <- 1:length(dateTable)
}
ensDates <- ensembleValidDates(ensembleData)
## match dates in data with dateTable
if (is.null(ensDates) || all(is.na(ensDates))) {
if (length(dates) > 1) stop("date ambiguity")
nObs <- nrow(ensembleData)
Dates <- rep( dates, nObs)
}
else {
## remove instances missing dates
if (any(M <- is.na(ensDates))) {
ensembleData <- ensembleData[!M,]
ensDates <- ensembleValidDates(ensembleData)
}
Dates <- as.character(ensDates)
L <- as.logical(match( Dates, dates, nomatch=0))
if (all(!L) || !length(L))
stop("model fit dates incompatible with ensemble data")
Dates <- Dates[L]
ensembleData <- ensembleData[L,]
nObs <- length(Dates)
}
nForecasts <- ensembleSize(ensembleData)
Quants <- matrix(NA, nObs, length(quantiles))
dimnames(Quants) <- list(ensembleObsLabels(ensembleData),
as.character(quantiles))
ensembleData <- ensembleForecasts(ensembleData)
l <- 0
for (d in dates) {
l <- l + 1
k <- K[l]
B <- fit$B[,k]
if (all(Bmiss <- is.na(B))) next
A <- fit$a[,k]
C <- fit$c[,k]
D <- fit$d[,k]
Q <- fit$q[,k]
I <- which(as.logical(match(Dates, d, nomatch = 0)))
for (i in I) {
f <- ensembleData[i,]
S.sq <- mean(f)
f <- c(1,f)
Mu <- c(A,B)%*%f #mean of gamma
Sig.sq <- C + D*S.sq #variance of gamma
Shp <- Mu^2/Sig.sq #shape of gamma
Scl <- Sig.sq/Mu #scale of gamma
zero.prec <- pgamma(Q,shape=Shp,scale=Scl)>=quantiles
Quants[i,is.na(zero.prec)] <-NA
Quants[i,(!is.na(zero.prec))&zero.prec] <- 0
Quants[i,(!is.na(zero.prec))&(!zero.prec)] <- (qgamma(quantiles, shape=Shp, scale=Scl)-Q)[(!is.na(zero.prec))&(!zero.prec)]
}
}
if (any(is.na(Quants))) warning("NAs in quantiles values")
Quants
}
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