Nothing
crps.ensembleBMAgamma <-
function(fit, ensembleData, dates=NULL, nSamples=10000, seed=NULL, ...)
{
#
# copyright 2006-present, University of Washington. All rights reserved.
# for terms of use, see the LICENSE file
#
if (!is.null(seed)) set.seed(seed)
powfun <- function(x,power) x^power
powinv <- function(x,power) x^(1/power)
weps <- 1.e-4
if (is.null(nSamples)) nSamples <- 10000
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)
Q <- as.vector(quantileForecast( fit, ensembleData, dates = dates))
if (any(is.na(Q))) stop("NAs in forecast") # fix like ensembleBMAgamma0
obs <- dataVerifObs(ensembleData)
nForecasts <- ensembleSize(ensembleData)
crpsSim <- rep(NA, nObs)
names(crpsSim) <- dataObsLabels(ensembleData)
members <- ensembleMembers(ensembleData)
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$varCoefs[2,d]*f)^2
fTrans <- sapply(f, powfun, power = fit$power)
MEAN <- apply(rbind(1, fTrans) * fit$biasCoefs[,d], 2, sum)
RATE <- MEAN/VAR
SHAPE <- MEAN*RATE
W <- WEIGHTS
if (any(M)) {
W <- W + weps
W <- W[!M] / sum(W[!M])
}
if (sum(!M) > 1) {
SAMPLES <- sample( (1:nForecasts)[!M], size = nSamples,
replace = TRUE, prob = W)
}
else {
SAMPLES <- rep((1:nForecasts)[!M], nSamples)
}
tab <- rep(0, nForecasts)
names(tab) <- members
for (j in seq(along = tab)) tab[j] <- sum(SAMPLES == j)
SAMPLES[] <- NA
jj <- 0
for (j in seq(along = tab)) {
nsamp <- tab[j]
if (nsamp == 0) next
SAMPLES[jj + 1:nsamp] <- rgamma(nsamp,shape=SHAPE[j],rate=RATE[j])
jj <- jj + nsamp
}
nz <- SAMPLES != 0
if (any(nz)) SAMPLES[nz] <- sapply(SAMPLES[nz], powinv, power=fit$power)
# crps2 approximates a term that is quadratic in the number of members
crps1 <- mean(abs(SAMPLES - obs[i]))
crps2 <- mean(abs(diff(sample(SAMPLES))))
crpsSim[i] <- crps1 - crps2/2
}
}
##crpsSim <- mean(crpsSim, na.rm = TRUE)
crpsCli <- sapply(obs, function(x,Y) mean(abs(Y-x)), Y = obs)
##crpsCli <- mean(crpsCli - mean(crpsCli)/2)
crpsCli <- crpsCli - mean(crpsCli)/2
crpsEns1 <- apply(abs(sweep(ensembleData,MARGIN=1,FUN ="-",STATS=obs))
,1,mean,na.rm=TRUE)
if (nrow(ensembleData) > 1) {
crpsEns2 <- apply(apply(ensembleData, 2, function(z,Z)
apply(abs(sweep(Z, MARGIN = 1, FUN = "-", STATS = z)),1,sum,na.rm=TRUE),
Z = ensembleData),1,sum, na.rm = TRUE)
}
else {
crpsEns2 <- sum(sapply(as.vector(ensembleData),
function(z,Z) sum( Z-z, na.rm = TRUE),
Z = as.vector(ensembleData)), na.rm = TRUE)
}
##crpsEns <- mean(crpsEns1 - crpsEns2/(2*(nForecasts*nForecasts)))
crpsEns <- crpsEns1 - crpsEns2/(2*(nForecasts*nForecasts))
#cbind(climatology = crpsCli, ensemble = crpsEns, BMA = crpsSim)
cbind(ensemble = crpsEns, BMA = crpsSim)
}
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