faces2: Chernoff Faces

Description

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

Usage

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), ...)

Value

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

Arguments

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.

Author

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

Details

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.

References

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

Examples

Run this code

  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 )
}

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