https://github.com/cran/ensembleBMA
Tip revision: 2a27e1ae449aad50128f973b80aa7ae8a6a4a868 authored by Chris Fraley on 08 November 2011, 00:00:00 UTC
version 5.0.2
version 5.0.2
Tip revision: 2a27e1a
crps.ensembleBMAnormal.R
crps.ensembleBMAnormal <-
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
#
weps <- 1.e-4
erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
absExp <- function(mu, sig)
{
(sqrt(2)* sig)*exp(-(mu/sig)^2/2)/sqrt(pi) +
mu * erf((sqrt(2)*mu)/(2*sig))
}
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)
members <- ensembleMembers(ensembleData)
CRPS <- crpsSim <- sampleMedian <- rep(NA, nObs)
names(crpsSim) <- names(sampleMedian) <- dataObsLabels(ensembleData)
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)
}
VAR <- SD*SD
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
# Expression of the CRPS formula and the E|x| if x ~ N(mu,sigma^2)
# CRPS = .5 sum( sum( w(i)w(j) a( u(i) - u(j), sigma(i)^2 + sigma(j)^2) ) )
# - sum( w(i) a( mu(i) - obs, sigma(i)^2 )
# here, a(u, sigma^2) is from E|X| with X ~ N(u, sigma^2)
# Using Maple, I get Expected value of abs( X ) with X ~ N > >
# (sigma*sqrt(2)*exp(-1/2/sigma^2*mu^2)+mu*erf(1/2/sigma*mu*2^(1/2))
# *sqrt(Pi)) > / Pi^(1/2) > >
# where erf is the error function.
if (is.null(nSamples)) {
W <- WEIGHTS
if (any(M)) {
W <- W + weps
W[!M] <- W[!M] / sum(W[!M])
}
crps1 <- crps2 <- 0
# Begin computing the first term in the CRPS formula.
# This is a double sum since it is over w(i)*w(j) for all i and j.
for (f1 in (1:nForecasts)[!M])
{
for (f2 in (1:nForecasts)[!M])
{
tvar <- VAR[f1] + VAR[f2] # total variance
tsd <- sqrt(tvar) # total standard deviation
tmean <- MEAN[f1] - MEAN[f2]
temp <- absExp(tmean,tsd)
term <- (W[f1]*W[f2])*temp
crps2 <- crps2 + term
}
tvar <- VAR[f1] # total variance
tsd <- sqrt(tvar) # total standard deviation
tmean <- MEAN[f1] - obs[i]
crps1 <- crps1 + W[f1]*absExp(tmean,tsd)
}
# Using Szekely's expression for the CRPS,
# the first sum and second are put together to compute the CRPS.
CRPS[i] <- crps1 - crps2/2
}
else {
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) next
SAMPLES[jj + 1:nsamp] <- rnorm( nsamp, MEAN[j], SD[j])
jj <- jj + nsamp
}
# sampleMean[i] <- mean(SAMPLES)
sampleMedian[i] <- median(SAMPLES)
# 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
}
}
}
crpsBMA <- if (is.null(nSamples)) CRPS else crpsSim
crpsCli <- sapply(obs, function(x,Y) mean(abs(Y-x)), Y = obs)
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 <- crpsEns1 - crpsEns2/(2*(nForecasts*nForecasts))
#cbind(climatology = crpsCli, ensemble = crpsEns, BMA = crpsBMA)
cbind(ensemble = crpsEns, BMA = crpsBMA)
}