R/pit.fitBMAnormal.R

In ensembleBMA: Probabilistic Forecasting using Ensembles and Bayesian Model Averaging

pit.fitBMAnormal <-
function(fit, ensembleData, dates=NULL, ...) 
{
#
# copyright 2006-present, University of Washington. All rights reserved.
# for terms of use, see the LICENSE file
#
 weps <- 1.e-4

 if (!is.null(dates)) warning("dates ignored")

 ensembleData <- ensembleData[,matchEnsembleMembers(fit,ensembleData)]

 M <- !dataNA(ensembleData,dates=FALSE)
 if (!all(M)) ensembleData <- ensembleData[M,]

 obs <- dataVerifObs(ensembleData)
 nObs <- length(obs)

 PIT <- numeric(nObs)
 names(PIT) <- dataObsLabels(ensembleData)

 obs <- dataVerifObs(ensembleData)
 nForecasts <- ensembleSize(ensembleData)
 ensembleData <- ensembleForecasts(ensembleData)

 WEIGHTS <- fit$weights
     
 if (!all(Wmiss <- is.na(WEIGHTS))) {
     
    SD <- if (!is.null(dim(fit$sd))) {
            fit$sd
          }
          else rep(fit$sd, nForecasts)

    for (i in 1:nObs) {
    
       f <- ensembleData[i,]

       M <- is.na(f) | Wmiss
     
       MEAN <- apply(rbind(1, f) * fit$biasCoefs, 2, sum)

       W <- WEIGHTS
       if (any(M)) {
         W <- W + weps
         W <- W[!M]/sum(W[!M])
       }

       PIT[i] <- cdfBMAnormal(obs[i], WEIGHTS = W, 
                              MEAN = MEAN[!M], SD = SD[!M]) 

    }

 }

 PIT
}