scattMiss(x, delimiter = NULL, side = 1, col = c("skyblue","red",
    "orange","lightgrey"), alpha = NULL, lty = c("dashed","dotted"),
    lwd = par("lwd"), quantiles = c(0.5, 0.975), inEllipse = FALSE,
    zeros = FALSE, xlim = NULL, ylim = NULL, main = NULL, sub = NULL,
    xlab = NULL, ylab = NULL, interactive = TRUE, ...)matrix or data.frame with two columns.x needs
    	to have colnames). If given, it is used to determine the
    	corside=1, a rug representation and vertical lines are 
    plotted for the missing/imputed values in the second variable; if side=2, a 
    rug representation and horizontal lines for the missing/imputed values in the first 
   NULL.  This can be used to prevent overplotting.NULL to suppress plotting 
    ellipses (see quantiles is NULL or if there are imputed values.TRUE, only the non-zero observations are used for computing the 
    tolerance ellipses.  If a siside argument can 
    be changed interactively (see par).side argument.  The lines are 
  thereby drawn at the observed x- or y-value. In case of imputed values, they
  will additionally be highlighted in the scatterplot. Supplementary, percentage
  coverage ellipses can be drawn to give a clue about the shape of the
  bivariate data distribution.
  
  If interactiveis TRUE, clicking in the bottom margin redraws 
  the plot with information about missing/imputed values in the first variable and 
  clicking in the left margin redraws the plot with information about missing/imputed 
  values in the second variable.  Clicking anywhere else in the plot quits the 
  interactive session.marginplotdata(tao, package = "VIM")
## for missing values
scattMiss(tao[,c("Air.Temp", "Humidity")])
## for imputed values
scattMiss(kNN(tao[,c("Air.Temp", "Humidity")]), delimiter = "_imp")Run the code above in your browser using DataLab