Nothing
pit.ensembleBMAgamma0 <-
function(fit, ensembleData, dates = NULL, randomizeATzero = FALSE, ...)
{
#
# copyright 2006-present, University of Washington. All rights reserved.
# for terms of use, see the LICENSE file
#
powfun <- function(x,power) x^power
powinv <- function(x,power) x^(1/power)
weps <- 1.e-4
matchITandFH(fit,ensembleData)
ensembleData <- ensembleData[,matchEnsembleMembers(fit,ensembleData)]
M <- !dataNA(ensembleData)
if (!all(M)) ensembleData <- ensembleData[M,]
fitDates <- modelDates(fit)
M <- matchDates( fitDates, ensembleValidDates(ensembleData), dates)
if (!all(M$ens)) ensembleData <- ensembleData[M$ens,]
if (!all(M$fit)) fit <- fit[fitDates[M$fit]]
dates <- modelDates(fit)
Dates <- ensembleValidDates(ensembleData)
obs <- dataVerifObs(ensembleData)
nObs <- length(obs)
nForecasts <- ensembleSize(ensembleData)
PIT <- numeric(nObs)
names(PIT) <- dataObsLabels(ensembleData)
obs <- sapply( dataVerifObs(ensembleData), powfun, power = fit$power)
ensembleData <- ensembleForecasts(ensembleData)
l <- 0
for (d in dates) {
l <- l + 1
WEIGHTS <- fit$weights[,d]
if (all(Wmiss <- is.na(WEIGHTS))) next
I <- which(as.logical(match(Dates, d, nomatch = 0)))
for (i in I) {
f <- ensembleData[i,]
M <- is.na(f) | Wmiss
VAR <- fit$varCoefs[1,d] + fit$varCoefs[2,d]*f
fTrans <- sapply(f, powfun, power = fit$power)
MEAN <- apply(rbind(1, fTrans) * fit$biasCoefs[,,d], 2, sum)
PROB0 <- sapply(apply(rbind( 1, fTrans, f == 0)*fit$prob0coefs[,,d],
2,sum), inverseLogit)
W <- WEIGHTS
if (any(M)) {
W <- W + weps
W <- W[!M]/sum(W[!M])
}
if (obs[i] == 0 && randomizeATzero) {
PIT[i] <- runif(1,min=0,max=sum(W*PROB0[!M]))
}
else {
PIT[i] <- cdfBMAgamma0( obs[i], WEIGHTS = W,
MEAN = MEAN[!M], VAR = VAR[!M], PROB0 = PROB0[!M])
}
}
}
PIT
}
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