oompaBase (version 3.2.10)

ColorCoding-class: Class "ColorCoding"

Description

A class for associating plotting symbols and colors with a logical vector or with levels of a factor.

Usage

ColorCoding(v, color, mark = 1)
colorCode(fac, colorScheme = 1:length(levels(fac)), mark = 1)

Value

The constructor returns a valid object of the ColorCoding

class.

The colorCode function provides a simple interface to associate colors and symbols with the levels of a factor. It returns a list of valid ColorCoding objects that can be passed directly to the constructor of a ColorCodedPair object.

Arguments

v

a logical vector

color

a string or integer representing a color

mark

an integer representing a plotting symbol, or list of plotting symbols that should be associated with levels of the factor fac

fac

A factor

colorScheme

A list of colors that should be associated with levels of the factor fac

Objects from the Class

Although objects can be created using new, the preferred method is to use the constructor function, ColorCoding. To create a set of colors and symbols associated with different levels of a factor, use the colorCode function.

Slots

v

a logical vector

color

a string or integer representing a color

mark

an integer representing a plotting symbol

Methods

There are no specialized methods for objects of this class; all activities can be performed by directly accessing the documented slots.

Author

Kevin R. Coombes , P. Roebuck

Details

It is often necessary with microarray data to produce multiple plots, where each point on the plot corresponds to a gene or a spot on the microarray. Across multiple plots, we often want to use symbols or colors to mark subsets of he genes with certain properties. The ColorCoding class works in tandem with the ColorCodedPair class to make it easier to maintain consistent plotting conventions across multiple graphs.

See Also

ColorCodedPair

Examples

showClass("ColorCoding")

theta <- (0:360)*pi/180
x <- cos(theta)
y <- sin(theta)
xp <- x > 0
yp <- y > 0
colors <- list(ColorCoding(xp&yp, oompaColor$BORING),
               ColorCoding(xp&!yp, oompaColor$TOP.TEN),
               ColorCoding(!xp&yp, oompaColor$BOTTOM.TEN),
               ColorCoding(!xp&!yp, oompaColor$CONFIDENCE.CURVE))
plot(ColorCodedPair(x, y, colors))

plot(ColorCodedPair(theta, x, colors))

plot(ColorCodedPair(theta, y, colors),
     xlab='angle in radians', ylab='sine', main='colored sine')

fac <- factor(rep(c('left', 'right'), c(180, 181)))
colors <- colorCode(fac, c('blue', 'red'))
plot(ColorCodedPair(x, y, colors))

## cleanup
rm(x, y, xp, yp, theta, colors, fac)

colorList <- c(oompaColor$BORING, oompaColor$SIGNIFICANT,
               oompaColor$EXPECTED, oompaColor$OBSERVED,
               oompaColor$PERMTEST, oompaColor$FITTED,
               oompaColor$CENTRAL.LINE, oompaColor$CONFIDENCE.CURVE,
               oompaColor$BAD.REPLICATE, oompaColor$WORST.REPLICATE,
               oompaColor$FOLD.DIFFERENCE, oompaColor$BAD.REPLICATE.RATIO,
               oompaColor$TOP.TEN, oompaColor$BOTTOM.TEN,
               oompaColor$TOP.TEN.SOLO, oompaColor$BOTTOM.TEN.SOLO
               )
plot(c(1,4), c(1,4), type='n')
for (i in 1:4) {
  for (j in 1:4) {
    points(i,j, col=colorList[i + 4*(j-1)], pch=16, cex=4)
  }
}
rm(colorList, i, j)