R/ROCplot.R

In helophilus/ColsTools: A variety of convenience tools and short-cuts

#' Simple ROC curve with optimal threshold and AUC approximation
#'
#' \code{ROCplot(predictions, truth, N = 100, nulline = TRUE,...)}
#'
#' @param predictions is prediction values between 0 and 1
#' @param truth the actual 0 and 1 values
#' @param N number of candidate thresholds; default is 100
#' @param nulline whether to plot a line with slope=1
#' @param ... additional arguments passed to plot
#'
#' @details Plots a ROC curve. Also gives optimal cut point (several if
#' they are equal in effectiveness) and area under the curve by the
#' trapezoid method, so accuracy improves with greater N.

ROCplot = function(predictions, truth, N = 100, nullline = TRUE,...){
  Sens = Spec = Acc = numeric(N+1)
  for(a in 0:N){
    T = table(factor(as.numeric(predictions>(a/N)),levels=0:1),
              factor(truth))
    Sens[a+1] = T[2,2]/sum(T[,2])
    Spec[a+1] = T[1,1]/sum(T[,1])
    Acc[a+1] = sum(diag(T))/sum(T)
  }
  plot(Spec,Sens,type="l", xlim=1:0, asp = 1,...)
  if(nullline==TRUE)abline(1,-1,lty=2)
  mx = which(Acc == max(Acc))
  span = 0:N/N
  cutoffs = span
  mxcutoff = cutoffs[mx]
  cat("\n", "optimal cut point is: ",mxcutoff, "\n")
  H = hist(Sens, breaks = span, plot = FALSE)$counts
  # calculate area under curve using trapezoidal method
  # areas of triangle and rectangle between adjacent points
  DX = c(0,diff(Sens))
  DY = c(0,diff(Spec))
  DY1 = c(0,DY)[1:length(DY)]
  cat("\n", "approx area under curve: ",
      sum(DX*DY/2 + Sens*DY1), "\n")
}