faces2: Chernoff Faces
In TeachingDemos: Demonstrations for Teaching and Learning

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Plot Chernoff Faces of the dataset, rows represent subjects/observations, columns represent variables.

faces2(mat, which = 1:ncol(mat), labels = rownames(mat),
  nrows = ceiling(nrow(mat)/ncols), ncols = ceiling(sqrt(nrow(mat))),
  byrow = TRUE, scale = c("columns", "all", "center", "none"),
  fill = c(0.5, 0.5, 1, 0.5, 0.5, 0.3, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
           0.5, 0.5, 0.5, 0.5, 1, 0.5), ...)

`mat`	Matrix containing the data to plot.
`which`	Which columns correspond to which features (see details).
`labels`	Labels for the individual faces
`nrows`	Number of rows in the graphical layout
`ncols`	Number of columns in the graphical layout
`byrow`	Logical, should the faces be drawn rowwise or columnwise.
`scale`	Character, how should the data be scaled.
`fill`	What value to use for features not assocaiated with a column of data.
`...`	Additional arguments passed on to plotting functions.

The features are: 1 Width of center 2 Top vs. Bottom width (height of split) 3 Height of Face 4 Width of top half of face 5 Width of bottom half of face 6 Length of Nose 7 Height of Mouth 8 Curvature of Mouth (abs < 9) 9 Width of Mouth 10 Height of Eyes 11 Distance between Eyes (.5-.9) 12 Angle of Eyes/Eyebrows 13 Circle/Ellipse of Eyes 14 Size of Eyes 15 Position Left/Right of Eyeballs/Eyebrows 16 Height of Eyebrows 17 Angle of Eyebrows 18 Width of Eyebrows

The face plotting routine needs the data values to be between 0 and 1 (inclusive). The scale option controls how scaling will be done on mat: "columns" scales each column to range from 0 to 1, "all" scales the entire dataset to vary from 0 to 1, "center" scales each column so that the mean of the column becomes 0.5 and all other values are between 0 and 1, and "none" does no scaling assuming that the data has already been scaled.

This function is run for its side effect of plotting and does not return anything.

If you choose to not scale the data and any data values are outside of the 0 to 1 range, then strange things may happen.

This function is based on code found on the internet, the good things come from there, any problems are likely due to my (Greg's) tweaking.

Original code by ; current implementation by Greg Snow 538280@gmail.com

Chernoff, H. (1973): The use of faces to represent statistiscal assoziation, JASA, 68, pp 361–368.

faces

  faces2(matrix( runif(18*10), nrow=10), main='Random Faces')

if(interactive()){
  tke2 <- rep( list(list('slider',from=0,to=1,init=0.5,resolution=0.1)), 18)
  names(tke2) <- c('CenterWidth','TopBottomWidth','FaceHeight','TopWidth',
	'BottomWidth','NoseLength','MouthHeight','MouthCurve','MouthWidth',
	'EyesHeight','EyesBetween','EyeAngle','EyeShape','EyeSize','EyeballPos',
	'EyebrowHeight','EyebrowAngle','EyebrowWidth')
  tkfun2 <- function(...){
	tmpmat <- rbind(Min=0,Adjust=unlist(list(...)),Max=1)
	faces2(tmpmat, scale='none')
  }

  tkexamp( tkfun2, list(tke2), plotloc='left', hscale=2, vscale=2 )
}