https://github.com/cran/ensembleBMA
Tip revision: 039bcb56eec51675e5f29d59983d3cbdfd883479 authored by Chris Fraley on 15 September 2009, 00:00:00 UTC
version 4.3.3
version 4.3.3
Tip revision: 039bcb5
brierScore.fitBMAgamma0.R
`brierScore.fitBMAgamma0` <-
function(fit, ensembleData, thresholds, dates=NULL, ...)
{
powfun <- function(x,power) x^power
powinv <- function(x,power) x^(1/power)
weps <- 1.e-4
if (!is.null(dates)) warning("dates ignored")
M <- matchEnsembleMembers(fit,ensembleData)
nForecasts <- ensembleSize(ensembleData)
if (!all(M == 1:nForecasts)) ensembleData <- ensembleData[,M]
# remove instances missing all forecasts or obs
M <- apply(ensembleForecasts(ensembleData), 1, function(z) all(is.na(z)))
M <- M | is.na(ensembleVerifObs(ensembleData))
ensembleData <- ensembleData[!M,]
if (is.null(y <- ensembleVerifObs(ensembleData)))
stop("verification observations required")
#nObs <- length(y)
nObs <- ensembleNobs(ensembleData)
ensembleData <- ensembleForecasts(ensembleData)
x <- sapply(apply( ensembleData, 1, mean, na.rm = TRUE),
powfun, power = fit$power)
MAT <- outer(y, thresholds, "<=")
bsClimatology <- apply(sweep(MAT, MARGIN = 2, FUN = "-",
STATS = apply(MAT,2,mean))^2, 2, mean)
bsVoting <- apply((t(apply(ensembleData, 1, function(z, thresholds)
apply(outer(z, thresholds, "<="), 2, mean),
thresholds = thresholds)) - MAT)^2, 2, mean)
# fit doesn't have a training period so logistic fit to all data
logisticFit <- sapply( thresholds,
function(thresh, x, y)
glm((y <= thresh) ~ x,family=binomial(logit))$coef,
x = x, y = y)
logisticFit[2,][is.na(logisticFit[2,])] <- 0
MAT <- apply(logisticFit, 2, function(coefs,x)
sapply(coefs[1] + coefs[2]*x, inverseLogit),
x = x) - outer(y, thresholds, "<=")
bsLogistic <- apply(MAT^2, 2, mean)
MAT <- matrix( NA, nObs, length(thresholds))
dimnames(MAT) <- list(NULL, as.character(thresholds))
# BMA Brier Scores
WEIGHTS <- fit$weights
if (!all(Wmiss <- is.na(WEIGHTS))) {
for (i in 1:nObs) {
f <- ensembleData[i,]
M <- is.na(f) | Wmiss
VAR <- fit$varCoefs[1] + fit$varCoefs[2]*f
fTrans <- sapply(f, powfun, power = fit$power)
MEAN <- apply(rbind(1, fTrans) * fit$biasCoefs, 2, sum)
PROB0 <- sapply(apply(rbind( 1, fTrans, f == 0)*fit$prob0coefs,
2,sum), inverseLogit)
W <- WEIGHTS
if (any(M)) {
W <- W + weps
W <- W[!M] / sum(W[!M])
}
MAT[i,] <- sapply( sapply( thresholds, powfun, power = fit$power),
cdfBMAgamma0,
WEIGHTS=W, MEAN=MEAN[!M], VAR=VAR[!M], PROB0=PROB0[!M]) -
(y[i] <= thresholds)
}
}
bsBMA <- apply(MAT^2, 2, mean)
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,
logistic = bsLogistic,
bma = bsBMA)
}