R/map.soa.hdf.R

In DJL: Distance Measure Based Judgment and Learning

map.soa.hdf <-
function(xdata, ydata, date, rts = "crs",
                        wd = NULL, sg = "ssm", cv = "convex", mk = "dmu"){

  # Initial checks
  if(is.na(match(rts, c("crs", "vrs", "irs", "drs")))) stop('rts must be "crs", "vrs", "irs", or "drs".')
  if(is.na(match(sg, c("ssm", "max", "min"))))         stop('sg must be "ssm", "max", or "min".')
  if(is.na(match(cv, c("convex", "fdh"))))             stop('cv must be "convex" or "fdh".')
  if(is.na(match(mk, c("dmu", "eff"))))                stop('mk must be either "dmu" or "eff".')

  # Parameters
  xdata <- as.matrix(xdata)
  ydata <- as.matrix(ydata)
  date  <- if(!is.null(date)) as.matrix(date)
  n     <- nrow(xdata)
  m     <- ncol(xdata)
  s     <- ncol(ydata)
  wd    <- if(is.null(wd)) matrix(c(0), ncol = s) else matrix(wd, 1)
  rts   <- ifelse(cv == "fdh", "vrs", rts)
  o     <- matrix(c(1:n), ncol = 1) # original data order
  ud    <- sort(unique(date))
  l     <- length(ud)
  
  # Sort data ascending order
  x   <- xdata[order(date),, drop = F]
  y   <- ydata[order(date),, drop = F]
  d   <- date [order(date),, drop = F]
  o   <- o    [order(date),, drop = F]
  
  # Map frame
  map.soa <- matrix(NA, n, l, dimnames = list(NULL, ud)) 

  # Generate the map
  for(i in ud){
    # run
    hdf.t <- dm.hdf(subset(x, d <= i), subset(y, d <= i), rts, 
                    wd, 0, sg, subset(d, d <= i), cv)
    
    # SOA index
    #id.soa <- which(round(hdf.t$eff, 8) == 1) # if slacks are not concerned
    id.soa <- which(round(hdf.t$eff, 8) == 1 & 
                    rowSums(cbind(round(hdf.t$xslack, 8), 
                                  round(hdf.t$yslack, 8))) == 0)
    
    # Mapping
    if(mk == "dmu"){
      if(i == ud[1]){
        map.soa[1:length(id.soa), 1] <- o[id.soa]
      }else{
        p <- which(ud == i)
        for(k in 1:length(id.soa)){
          id.preb <- which(map.soa[, p - 1] == o[id.soa[k],])
          if(length(id.preb) > 0){
            map.soa[id.preb, p] <- o[id.soa[k],]
          }else{
            map.soa[sum(rowSums(map.soa, na.rm = T) > 0) + 1, p] <- o[id.soa[k],]
          }
        }
      }
    }else{
      gsoa <- if(i == ud[1]) id.soa else union(gsoa, id.soa)
      map.soa[1:length(gsoa), which(ud == i)] <- hdf.t$eff[gsoa,]
    }
  }
  
  # Prune the map
  map.soa           <- map.soa[1:max(which(!is.na(map.soa[, l]))),] 
  rownames(map.soa) <- if(mk == "dmu") unique(na.omit(c(map.soa))) else c(o[gsoa,]) 
  
  # Print
  print(map.soa)
}