brierScore.ensembleBMAnormal.R
`brierScore.ensembleBMAnormal` <-
function(fit, ensembleData, thresholds, dates = NULL, ...)
{
weps <- 1.e-4
M <- matchEnsembleMembers(fit,ensembleData)
nForecasts <- ensembleSize(ensembleData)
if (!all(M == 1:nForecasts)) ensembleData <- ensembleData[,M]
# remove instances missing all forecasts, obs, or dates
M <- apply(ensembleForecasts(ensembleData), 1, function(z) all(is.na(z)))
M <- M | is.na(ensembleVerifObs(ensembleData))
M <- M | is.na(ensembleDates(ensembleData))
ensembleData <- ensembleData[!M,]
if (is.null(y <- ensembleVerifObs(ensembleData)))
stop("verification observations required")
## remove instances missing all forecasts
M <- apply(ensembleForecasts(ensembleData), 1, function(z) all(is.na(z)))
M <- M | is.na(ensembleVerifObs(ensembleData))
ensembleData <- ensembleData[!M,]
## match specified dates with dateTable in fit
dateTable <- dimnames(fit$weights)[[2]]
if (!is.null(dates)) {
dates <- sort(unique(as.character(dates)))
if (length(dates) > length(dateTable))
stop("parameters not available for some dates")
K <- match( dates, dateTable, nomatch=0)
if (any(!K) || !length(K))
stop("parameters not available for some dates")
}
else {
dates <- dateTable
K <- 1:length(dateTable)
}
ensDates <- ensembleDates(ensembleData)
## match dates in data with dateTable
if (is.null(ensDates) || all(is.na(ensDates))) {
if (length(dates) > 1) stop("date ambiguity")
nObs <- nrow(ensembleData)
Dates <- rep( dates, nObs)
}
else {
## remove instances missing dates
if (any(M <- is.na(ensDates))) {
ensembleData <- ensembleData[!M,]
ensDates <- ensembleDates(ensembleData)
}
Dates <- as.character(ensDates)
L <- as.logical(match( Dates, dates, nomatch=0))
if (all(!L) || !length(L))
stop("model fit dates incompatible with ensemble data")
Dates <- Dates[L]
ensembleData <- ensembleData[L,]
nObs <- length(Dates)
}
y <- ensembleVerifObs(ensembleData)
nForecasts <- ensembleSize(ensembleData)
ensembleData <- ensembleForecasts(ensembleData)
MAT <- outer(y, thresholds, "<=")
bsClimatology <- apply(sweep(MAT, MARGIN = 2, FUN = "-",
STATS = apply(MAT,2,mean))^2, 2, mean, na.rm = TRUE)
bsVoting <- apply((t(apply(ensembleData, 1, function(z, thresholds)
apply(outer(z, thresholds, "<="), 2, mean, na.rm = TRUE),
thresholds = thresholds)) - MAT)^2, 2, mean, na.rm = TRUE)
MAT <- matrix( NA, nObs, length(thresholds))
dimnames(MAT) <- list(NULL, as.character(thresholds))
l <- 0
for (d in dates) {
# BMA Brier Scores
l <- l + 1
k <- K[l]
WEIGHTS <- fit$weights[,k]
if (all(Wmiss <- is.na(WEIGHTS))) next
SD <- if (!is.null(dim(fit$sd))) {
fit$sd[,k]
}
else rep(fit$sd[k], nForecasts)
I <- which(as.logical(match(Dates, d, nomatch = 0)))
for (i in I) {
f <- ensembleData[i,]
M <- is.na(f) | Wmiss
MEAN <- apply(rbind(1, f) * fit$biasCoefs[,,k], 2, sum)
W <- WEIGHTS
if (any(M)) {
W <- W + weps
W <- W[!M]/sum(W[!M])
}
MAT[i,] <- sapply( thresholds, cdfBMAnormal,
WEIGHTS = W, MEAN = MEAN[!M], SD = SD[!M]) -
(y[i] <= thresholds)
}
}
# locations at which forecasts are made (depends on training length and lag)
bsBMA <- apply(MAT^2, 2, mean, na.rm = TRUE)
safeDiv <- function(x,y) {
yzero <- !y
nz <- sum(yzero)
result <- rep(NA, length(y))
if (!nz) result <- x/y else result[!yzero] <- x[!yzero]/y[!yzero]
result
}
# data.frame(thresholds = thresholds,
# ensemble = 1 - safeDiv(bsVoting,bsClimatology),
# logistic = 1 - safeDiv(bsLogistic,bsClimatology),
# bma = 1 - safeDiv(bsBMA,bsClimatology))
data.frame(thresholds = thresholds,
climatology = bsClimatology,
ensemble = bsVoting,
bma = bsBMA)
}