vioplot.R

library(sm)
vioplot <- function(x,...,range=1.5,h=NULL,ylim=NULL,names=NULL, horizontal=FALSE,
                    col="magenta", border="black", lty=1, lwd=1, rectCol="black", 
                    colMed="white", pchMed=19, at, add=FALSE, wex=1,
                    drawRect=TRUE, medlwd = 3)
{
  # process multiple datas
  datas <- list(x,...)
  n <- length(datas)
  
  if(missing(at)) at <- 1:n
  
  # pass 1
  #
  # - calculate base range
  # - estimate density
  #
  
  # setup parameters for density estimation
  upper  <- vector(mode="numeric",length=n)
  lower  <- vector(mode="numeric",length=n)
  q1     <- vector(mode="numeric",length=n)
  q3     <- vector(mode="numeric",length=n)
  med    <- vector(mode="numeric",length=n)
  base   <- vector(mode="list",length=n)
  height <- vector(mode="list",length=n)
  baserange <- c(Inf,-Inf)
  
  # global args for sm.density function-call
  args <- list(display="none")
  
  if (!(is.null(h)))
    args <- c(args, h=h)
  
  for(i in 1:n) {
    data<-datas[[i]]
    
    # calculate plot parameters
    #   1- and 3-quantile, median, IQR, upper- and lower-adjacent
    data.min <- min(data)
    data.max <- max(data)
    q1[i]<-quantile(data,0.25)
    q3[i]<-quantile(data,0.75)
    med[i]<-median(data)
    iqd <- q3[i]-q1[i]
    upper[i] <- min( q3[i] + range*iqd, data.max )
    lower[i] <- max( q1[i] - range*iqd, data.min )
    
    #   strategy:
    #       xmin = min(lower, data.min))
    #       ymax = max(upper, data.max))
    #
    
    est.xlim <- c( min(lower[i], data.min), max(upper[i], data.max) )
    
    # estimate density curve
    smout <- do.call("sm.density", c( list(data, xlim=est.xlim), args ) )
    
    # calculate stretch factor
    #
    #  the plots density heights is defined in range 0.0 ... 0.5
    #  we scale maximum estimated point to 0.4 per data
    #
    hscale <- 0.4/max(smout$estimate) * wex
    
    # add density curve x,y pair to lists
    base[[i]]   <- smout$eval.points
    height[[i]] <- smout$estimate * hscale
    
    # calculate min,max base ranges
    t <- range(base[[i]])
    baserange[1] <- min(baserange[1],t[1])
    baserange[2] <- max(baserange[2],t[2])
    
  }
  
  # pass 2
  #
  # - plot graphics
  
  # setup parameters for plot
  if(!add){
    xlim <- if(n==1)
      at + c(-.5, .5)
    else
      range(at) + min(diff(at))/2 * c(-1,1)
    
    if (is.null(ylim)) {
      ylim <- baserange
    }
  }
  if (is.null(names)) {
    label <- 1:n
  } else {
    label <- names
  }
  
  boxwidth <- 0.05 * wex
  
  # setup plot
  if(!add)
    plot.new()
  if(!horizontal) {
    if(!add){
      plot.window(xlim = xlim, ylim = ylim)
      axis(2)
      axis(1,at = at, label=label )
    }
    
    #box()
    for(i in 1:n) {
      # plot left/right density curve
      polygon( c(at[i]-height[[i]], rev(at[i]+height[[i]])),
               c(base[[i]], rev(base[[i]])),
               col = col[i %% length(col) + 1], border=border[i], lty=lty, lwd=lwd)
      
      lines( c(at[i] - 0.15, at[i] + 0.15 ), 
              rep(med[i], 2), lwd = medlwd, col=colMed[i] )  #### plot median points
      if(drawRect){
        # plot IQR
        lines( at[c( i, i)], c(lower[i], upper[i]) ,lwd=lwd, lty=lty)
        
        # plot 50% KI box
        rect( at[i]-boxwidth/2, q1[i], at[i]+boxwidth/2, q3[i], col=rectCol)

        # plot median point
        points( at[i], med[i], pch=pchMed, col=colMed )
      }
    }
    
  }
  else {
    if(!add){
      plot.window(xlim = ylim, ylim = xlim)
      axis(1)
      axis(2,at = at, label=label )
    }
    
    for(i in 1:n) {
      # plot left/right density curve
      polygon( c(base[[i]], rev(base[[i]])),
               c(at[i]-height[[i]], rev(at[i]+height[[i]])),
               col = col[i %% length(col) + 1], border=border, lty=lty, lwd=lwd)
      lines( rep(med[i], 2),
             c(at[i] - 0.15, at[i] + 0.15 ), 
              lwd = 5, col=colMed[i], medlwd)  #### plot median points
      
      if(drawRect){
        # plot IQR
        lines( c(lower[i], upper[i]), at[c(i,i)] ,lwd=lwd, lty=lty)
        
        # plot 50% KI box
        rect( q1[i], at[i]-boxwidth/2, q3[i], at[i]+boxwidth/2,  col=rectCol)
        
        # plot median point
        points( med[i], at[i], pch=pchMed, col=colMed )
      }
    }
  }
  invisible (list( upper=upper, lower=lower, median=med, q1=q1, q3=q3))
}