R/roc.hdf.R

In DJL: Distance Measure Based Judgment and Learning

roc.hdf <-
function(xdata, ydata, date, t, rts = "crs",
                    wd = NULL, sg = "ssm", ftype = "d", cv = "convex"){
  
  # Initial checks
  if(t <= min(date))                                   stop('t is earlier than dataset.')
  if(max(date) < t)                                    stop('t is later than dataset.')
  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(ftype, c("d","s"))))                  stop('ftype must be either "d" or "s".')
  if(is.na(match(cv,  c("convex", "fdh"))))            stop('cv must be "convex" or "fdh".')
  
  # Parameters
  xdata <- as.matrix(xdata)
  ydata <- as.matrix(ydata)
  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
  r     <- tail(which(sort(date) <= t), 1)
  
  # 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]
  
  # Data frames
  eff_r     <- array(NA, c(n,1))
  eff_t     <- array(NA, c(n,1))
  lambda    <- array(NA, c(n,n))
  ed        <- array(NA, c(n,1))
  sl        <- array(NA, c(n,1))
  roc       <- array(NA, c(n,1))
  local_roc <- array(NA, c(n,1))
  
  # Loop for eff_r and eff_t
  for(i in unique(d[1:r])){
    # Run
    hdf_r                 <- dm.hdf(subset(x, d <= i), subset(y, d <= i), rts,
                                    wd, 0, sg, subset(d, d <= i), cv, which(d == i))
    eff_r[which(d == i),] <- hdf_r$eff[which(d == i),]
    if(i == d[r]){
      hdf_t               <- dm.hdf(subset(x, d <= i), subset(y, d <= i), rts,
                                     wd, 0, sg, subset(d, d <= i), cv)
      eff_t[1:r,]         <- hdf_t$eff[1:r,]
      lambda[1:r, 1:r]    <- hdf_t$lambda[1:r, 1:r]
    } 
  }
  
  # Effective date
  ed <- if(ftype == "s") rep(t, r) else lambda[, 1:r] %*% d[1:r,] / rowSums(lambda, na.rm=T)
  
  # RoC
  id_roc       <- which(round(eff_r[, 1], 8) == 1 & round(eff_t[, 1], 8) != 1 & ed[, 1] > d[, 1])
  delta_t      <- 1/(ed - d)
  roc[id_roc,] <- (1/eff_t[id_roc,])^delta_t[id_roc,]
  
  # RoC filter
  roc[roc[id_roc,] > 10,] <- NA
  avgroc                  <- mean(roc, na.rm = T)
  
  # RoC segmentation
  id_local_roc             <- which(colSums(lambda, na.rm = T) > 0)
  temp                     <- t(lambda[id_roc, id_local_roc, drop = F]) %*% roc[id_roc] / colSums(lambda[id_roc, id_local_roc, drop = F])
  temp[is.nan(temp),]      <- NA # For coding convenience, could be improved
  local_roc[id_local_roc,] <- temp
  
  # Sort results back to original order
  eff_r     <- eff_r[order(o),,           drop = F]
  eff_t     <- eff_t[order(o),,           drop = F]
  lambda    <- lambda[order(o), order(o), drop = F]
  ed        <- ed[order(o),,              drop = F]
  roc       <- roc[order(o),,             drop = F]
  local_roc <- local_roc[order(o),,       drop = F]
  
  results <- list(eff_r = eff_r, eff_t = eff_t, lambda_t = lambda, eft_date = ed,
                  roc_past = roc, roc_local = local_roc, roc_avg = avgroc)
  return(results)
}