R/as.bugs.array2.R

In R2jags: Using R to Run 'JAGS'

as.bugs.array2 <- function(sims.array, model.file=NULL, program="jags",
              DIC=FALSE, DICOutput=NULL, n.iter=NULL, n.burnin=0, n.thin=1, checkMissing=FALSE)
{
  ## 'sims.array' is supposed to be a 3-way array with
  # n.sims*n.chains*n.parameters simulations, and
  # the 3rd component of dimnames(x) should have the parameter names.

  ## From Andrew Gelman's bugs.r function
  ## a couple of lines commented out by Eduardo Leoni (see comment below)
  #require("R2WinBUGS")
  d <- dim(sims.array)
  n.keep       <- d[1]
  n.chains     <- d[2]
  n.parameters <- d[3]
  n.sims       <- n.keep*n.chains
  if (is.null(n.iter)){
    n.iter <- (n.keep + n.burnin)*n.thin
  }
  #
  parameter.names <- dimnames(sims.array)[[3]]
  if (is.null(parameter.names)) {
    parameter.names <- paste("P", 1:n.parameters, sep="")
    dimnames(sims.array)[[3]] <- parameter.names
  }
  parameters.to.save <- unique(sapply(strsplit(parameter.names, "\\["), "[", 1))
  #
  sims <- matrix(NA, n.sims, n.parameters)
  root.long <- character(n.parameters)
  indexes.long <- vector(n.parameters, mode = "list")
  for (i in 1:n.parameters) {
    temp <- .decode.parameter.name(parameter.names[i])
    root.long[i] <- temp$root
    indexes.long[[i]] <- temp$indexes
  }
  n.roots <- length(parameters.to.save)
  left.bracket.short <- as.vector(regexpr("[[]", parameters.to.save))
  right.bracket.short <- as.vector(regexpr("[]]", parameters.to.save))
  root.short <- ifelse(left.bracket.short == -1, parameters.to.save,
      substring(parameters.to.save, 1, left.bracket.short -1))
  dimension.short <- rep(0, n.roots)
  indexes.short <- vector(n.roots, mode = "list")
  n.indexes.short <- vector(n.roots, mode = "list")
  long.short <- vector(n.roots, mode = "list")
  length.short <- numeric(n.roots)
  for (j in 1:n.roots) {
      long.short[[j]] <- (1:n.parameters)[root.long == root.short[j]]
      length.short[j] <- length(long.short[[j]])
      if (length.short[j] == 0){
          stop(paste("parameter", root.short[[j]], "is not in the model"))
      }
      else if (length.short[j] > 1) {
          dimension.short[j] <- length(indexes.long[[long.short[[j]][1]]])
          n.indexes.short[[j]] <- numeric(dimension.short[j])
          for (k in 1:dimension.short[j]){
            n.indexes.short[[j]][k] <- length(unique(unlist(lapply(indexes.long[long.short[[j]]], .subset, k))))
          }
          length.short[j] <- prod(n.indexes.short[[j]])
          ## Modified by Eduardo Leoni
          ## this check fails if you take out a part of the simulations
          ## (for example, you don't want the array to have some of the
          ## parameters) so I took them out.
           if (length(long.short[[j]]) != length.short[j]){
             warning(paste("error/missing in parameter", root.short[[j]],"in parameters.to.save, \n   Be aware of the output results.\n"), noBreaks.=TRUE)
           }
          indexes.short[[j]] <- as.list(numeric(length.short[j]))
          for (k in 1:length.short[j]){
            indexes.short[[j]][[k]] <- indexes.long[[long.short[[j]][k]]]
          }
      }
  }
  rank.long <- unlist(long.short)
  # -----
  # yes, it's inefficient to do this, but for now I'm just letting this be as it is:
  for (k in 1:n.parameters) {
    sims[,k] <- as.vector(sims.array[,,k])
  }
  # ----
  dimnames(sims) <- list(NULL, parameter.names)
  summary <- monitor(sims.array, n.chains, keep.all = TRUE)
  last.values <- as.list(numeric(n.chains))

  for(nn in 1:length(n.indexes.short)){
    if(is.null(n.indexes.short[[nn]])){
      n.indexes.short[[nn]] <- 1
    }
  }


  for (i in 1:n.chains) {
    n.roots.0 <- if (DIC){
                   n.roots - 1
                }
                else{
                  n.roots
                }
    last.values[[i]] <- as.list(numeric(n.roots.0))
    names(last.values[[i]]) <- root.short[1:n.roots.0]
    for (j in 1:n.roots.0) {
      if (dimension.short[j] <= 1) {
        last.values[[i]][[j]] <- sims.array[n.keep, i, long.short[[j]]]
        names(last.values[[i]][[j]]) <- NULL
      }
      if (program=="jags"){
        last.values[[i]][[j]] <- array(sims.array[n.keep, i, long.short[[j]]], n.indexes.short[[j]])
      }
      ## only winbugs have to permute the array.
      else {
        last.values[[i]][[j]] <- aperm(array(sims.array[n.keep, i, long.short[[j]]], rev(n.indexes.short[[j]])), dimension.short[j]:1)
      }
    }
  }
  sims <- sims[sample(n.sims), , drop = FALSE]
  sims.list <- summary.mean <- summary.sd <- summary.median <- summary.025 <-  summary.975 <- vector(n.roots, mode = "list")
  names(sims.list) <- names(summary.mean) <- names(summary.sd) <- names(summary.median) <- names(summary.025) <- names(summary.975) <- root.short
  for (j in 1:n.roots) {
    if (length.short[j] == 1) {
        sims.list[[j]] <- sims[, long.short[[j]]]
        summary.mean[[j]] <- summary[long.short[[j]], "mean"]
        summary.sd[[j]] <- summary[long.short[[j]], "sd"]
        summary.median[[j]] <- summary[long.short[[j]], "50%"]
        ##ell: added 025 and 975
        summary.025[[j]] <- summary[long.short[[j]], "2.5%"]
        summary.975[[j]] <- summary[long.short[[j]], "97.5%"]
    }
    if (program=="bugs") {
      temp2 <- dimension.short[j]:1
      sims.list[[j]] <- aperm(array(sims[, long.short[[j]]], c(n.sims, rev(n.indexes.short[[j]]))), c(1, (dimension.short[j] + 1):2))
      summary.mean[[j]] <- aperm(array(summary[long.short[[j]], "mean"], rev(n.indexes.short[[j]])), temp2)
      summary.sd[[j]] <- aperm(array(summary[long.short[[j]], "sd"], rev(n.indexes.short[[j]])), temp2)
      summary.median[[j]] <- aperm(array(summary[long.short[[j]], "50%"], rev(n.indexes.short[[j]])), temp2)
      ##ell: added 025 and 975
#      summary.025[[j]] <- aperm(array(summary[long.short[[j]], "2.5%"], rev(n.indexes.short[[j]])), temp2)
#      summary.975[[j]] <- aperm(array(summary[long.short[[j]], "97.5%"], rev(n.indexes.short[[j]])), temp2)
    }
    if (program=="jags") {
      ##fix this list
      #sims.list[[j]] <- aperm(array(sims[, long.short[[j]]], c(n.sims, rev(n.indexes.short[[j]]))), c(1, (dimension.short[j] + 1):2))
      sims.list[[j]] <- array(sims[, long.short[[j]]], c(n.sims, n.indexes.short[[j]]))

      # this is a quick fix to the case where elements in levels are missing, ie empty cells in a parameter matrix 2024.3.31
      # the code is ugly. need to fix in the future.  Add two hiddent functions below in the end of the page
      if(checkMissing){
          missingCell <- .checkEmptyCell(n.indexes.short[[j]], long.short[[j]], parameter.names)
          if(length(missingCell)>1){
            for(s in 1:n.sims){
                sims.list[[j]][s,,][missingCell] <- NA
            }
          }
      }
      #sims.list[[j]] <- sims[, long.short[[j]]]
      summary.mean[[j]] <- array(summary[long.short[[j]],"mean"],n.indexes.short[[j]])
      summary.sd[[j]] <- array(summary[long.short[[j]],"sd"],n.indexes.short[[j]])
      summary.median[[j]] <- array(summary[long.short[[j]],"50%"],n.indexes.short[[j]])
      
      ##ell: added 025 and 975
#      summary.025[[j]] <- array(summary[long.short[[j]],"2.5%"],n.indexes.short[[j]])
#      summary.975[[j]] <- array(summary[long.short[[j]],"97.5%"],n.indexes.short[[j]])
        
    }
  }

  summary <- summary[rank.long,, drop = FALSE]
  #all <- list(n.chains = n.chains, n.iter = n.iter, n.burnin = n.burnin,
#        n.thin = n.thin, n.keep = n.keep, n.sims = n.sims,
#        sims.array = sims.array[, , rank.long, drop = FALSE], sims.list = sims.list,
#        sims.matrix = sims[, rank.long, drop = FALSE], summary = summary, mean = summary.mean,
#        sd = summary.sd, median = summary.median, root.short = root.short,
#        long.short = long.short, dimension.short = dimension.short,
#        indexes.short = indexes.short, last.values = last.values,
#        is.DIC=DIC, p02.5=summary.025, p97.5=summary.975)

  all <- list(n.chains = n.chains, n.iter = n.iter, n.burnin = n.burnin,
      n.thin = n.thin, n.keep = n.keep, n.sims = n.sims,
      sims.array = sims.array[,,rank.long, drop = FALSE], sims.list = sims.list,
      sims.matrix = sims[, rank.long, drop = FALSE], summary = summary, mean = summary.mean,
      sd = summary.sd, median = summary.median, root.short = root.short,
      long.short = long.short, dimension.short = dimension.short,
      indexes.short = indexes.short, last.values = last.values, program=program,
      model.file=model.file)

  if(DIC && is.null(DICOutput)) { ## calculate DIC from deviance
    deviance <- all$sims.array[, , "deviance", drop = FALSE]
    dim(deviance) <- dim(deviance)[1:2]
# Modified by GB to rename pD to pV
    pV <- numeric(n.chains)
    DIC <- numeric(n.chains)
    for (i in 1:n.chains) {
      pV[i] <- var(deviance[, i])/2
      DIC[i] <- mean(deviance[, i]) + pV[i]
    }
    all <- c(all, list(isDIC=TRUE, DICbyR=TRUE,  pV=mean(pV), DIC=mean(DIC)))
  }
  #' GB: This is ported by R2WinBUGS/R2OpenBUGS and assumes that the model
  #' is actually run using BUGS, which would mean you can actually compute
  #' pD using BUGS output. In this case, because 'DICOutput' is set to 'NULL'
  #' this bit is kind of ignored. But the drawback is that the only possibility
  #' to compute pD is to go through 'rjags::dic.samples()'. I have added an
  #' optional call to 'rjags::dic.samples' in 'jags' to compute pD
  else if(DIC && !is.null(DICOutput)) { ## use DIC from BUGS
    all <- c(all, list(isDIC=TRUE, DICbyR=FALSE,
                       pD=DICOutput[nrow(DICOutput),4],
                       DIC=DICOutput[nrow(DICOutput),3]))
  }
  else {
    all <- c(all, isDIC=FALSE)
  }
  class(all) <- "bugs"
  all
}


.checkEmptyCell <- function(n.indexes.short, long.short, parameter.names){
    size1 <- prod(n.indexes.short)
    size2 <- length(long.short)
    if(size1 > size2){
        paraNames <- parameter.names[long.short]
        indicesLst <- lapply(paraNames, .extract_indices)
        indices <- do.call(rbind, indicesLst)
        paramMatrix <- matrix(FALSE, nrow = n.indexes.short[1], ncol = n.indexes.short[2])
        paramMatrix[indices] <- TRUE
        missingCell <- which(!paramMatrix, arr.ind = TRUE) 
    }else{
        missingCell <- NA
    }
    return(missingCell)
}

.extract_indices <- function(parameter.names) {
  #indices <-  gregexpr("\\d+", parameter.names)[[1]]
  #indices <- as.numeric(indices)
  #indices <- gsub("\\d+", "", parameter.names) # Extract only digits
  #indices <- as.numeric(indices)) # Split into individual digits and convert to numeric
  indices <- str_match(parameter.names, "\\[(\\d+),(\\d+)\\]")[,-1]
  indices <- matrix(as.numeric(indices), ncol = 2, byrow = TRUE)
  #indices <- as.numeric(indices)
  return(indices)
}