Nothing
cdf.ensembleBMAnormal <-
function(fit, ensembleData, values, dates = NULL, ...)
{
#
# copyright 2006-present, University of Washington. All rights reserved.
# for terms of use, see the LICENSE file
#
weps <- 1.e-4
matchITandFH(fit,ensembleData)
ensembleData <- ensembleData[,matchEnsembleMembers(fit,ensembleData)]
M <- !dataNA(ensembleData,observations=FALSE)
if (!all(M)) ensembleData <- ensembleData[M,]
fitDates <- modelDates(fit)
M <- matchDates( fitDates, ensembleValidDates(ensembleData), dates=dates)
if (!all(M$ens)) ensembleData <- ensembleData[M$ens,]
if (!all(M$fit)) fit <- fit[fitDates[M$fit]]
if (is.null(dates)) dates <- modelDates(fit)
Dates <- ensembleValidDates(ensembleData)
nObs <- nrow(ensembleData)
if (!nObs) stop("no data")
Dates <- ensembleValidDates(ensembleData)
nForecasts <- ensembleSize(ensembleData)
CDF <- matrix( NA, nrow = nObs, ncol = length(values))
dimnames(CDF) <- list(dataObsLabels(ensembleData), as.character(values))
ensembleData <- ensembleForecasts(ensembleData)
l <- 0
for (d in dates) {
l <- l + 1
WEIGHTS <- fit$weights[,d]
if (all(Wmiss <- is.na(WEIGHTS))) next
SD <- if (!is.null(dim(fit$sd))) {
fit$sd[,d]
}
else rep(fit$sd[d], nForecasts)
I <- which(as.logical(match(Dates, d, nomatch = 0)))
for (i in I) {
f <- ensembleData[i,]
MEAN <- apply(rbind(1, f) * fit$biasCoefs[,,d], 2, sum)
M <- is.na(f) | Wmiss
W <- WEIGHTS
if (any(M)) {
W <- W + weps
W <- W[!M]/sum(W[!M])
}
CDF[i,] <- sapply( values, cdfBMAnormal,
WEIGHTS = W, MEAN = MEAN[!M], SD = SD[!M])
}
}
CDF
}
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