Nothing
cdf.fitMOStruncnormal <-
function(fit, ensembleData, values, dates = NULL, ...)
{
M <- matchEnsembleMembers(fit,ensembleData)
nForecasts <- ensembleSize(ensembleData)
if (!all(M == 1:nForecasts)) ensembleData <- ensembleData[,M]
## remove instances missing all forecasts or dates
M <- apply(ensembleForecasts(ensembleData), 1, function(z) all(is.na(z)))
ensembleData <- ensembleData[!M,]
nObs <- nrow(ensembleData)
if (!is.null(dates)) warning("dates ignored")
CDF <- matrix(NA, nObs, length(values))
dimnames(CDF) <- list(ensembleObsLabels(ensembleData),as.character(values))
Mu <- rep(NA,nObs)
Sig <- rep(NA,nObs)
ensembleData <- ensembleForecasts(ensembleData)
x <- c(fit$a,fit$B)
A <- cbind(rep(1,nObs),ensembleData)
S.sq <- apply(ensembleData,1,var)
Mu <- A%*%x
Sig <- sqrt(rep(fit$c,nObs) + rep(fit$d,nObs)*S.sq)
for (i in 1:length(values)){
CDF[,i] <- (pnorm(values[i], mean = Mu, sd = Sig) + pnorm(Mu/Sig)-1) *
(values[i] >= 0) / pnorm(Mu/Sig)
}
CDF
}
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