1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
|
\name{faces2}
\alias{faces2}
\alias{face2.plot}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ 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), ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{mat}{ Matrix containing the data to plot. }
\item{which}{ Which columns correspond to which features (see details). }
\item{labels}{ Labels for the individual faces }
\item{nrows}{ Number of rows in the graphical layout }
\item{ncols}{ Number of columns in the graphical layout }
\item{byrow}{ Logical, should the faces be drawn rowwise or columnwise.
}
\item{scale}{ Character, how should the data be scaled. }
\item{fill}{ What value to use for features not assocaiated with a
column of data. }
\item{\dots}{ Additional arguments passed on to plotting functions. }
}
\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 \code{scale} option controls how scaling will be
done on \code{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.
}
\value{
This function is run for its side effect of plotting and does not
return anything.
}
\references{ Chernoff, H. (1973): The use of faces to represent statistiscal assoziation,
JASA, 68, pp 361--368. }
\author{ Original code by ; current implementation by Greg Snow \email{538280@gmail.com} }
\note{ 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.
}
\seealso{\code{\link{faces}}}
\examples{
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 )
}
}
\keyword{ hplot }% at least one, from doc/KEYWORDS
|
