R/cdf.fitBMAgamma0.R

In ensembleBMA: Probabilistic Forecasting using Ensembles and Bayesian Model Averaging

cdf.fitBMAgamma0 <-
function(fit, ensembleData, values, dates=NULL, ...) 
{
#
# copyright 2006-present, University of Washington. All rights reserved.
# for terms of use, see the LICENSE file
#
 
 powfun <- function(x,power) x^power
 powinv <- function(x,power) x^(1/power)

 weps <- 1.e-4

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

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

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

 nObs <- nrow( ensembleData)
 if (!nObs) stop("no data")

 CDF <- matrix( NA, nrow = nObs, ncol = length(values))
 dimnames(CDF) <- list(dataObsLabels(ensembleData), as.character(values)) 

 nForecasts <- ensembleSize(ensembleData)

 ensembleData <- ensembleForecasts(ensembleData)

 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])
       }

       CDF[i,] <- sapply( sapply( values, powfun, power = fit$power), 
             cdfBMAgamma0, WEIGHTS = W, MEAN = MEAN[!M], VAR = VAR[!M], 
                           PROB0 = PROB0[!M]) 
    }

 }

 CDF
}