R/brierScore.fitBMAgamma0.R

In ensembleBMA: Probabilistic Forecasting using Ensembles and Bayesian Model Averaging

brierScore.fitBMAgamma0 <-
function(fit, ensembleData, thresholds, 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,dates=FALSE)
 if (!all(M)) ensembleData <- ensembleData[M,]

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

 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)

}