plot.gmdm: S3 method for class 'gmdm'

In GMD: Generalized Minimum Distance of distributions

Description

S3 method for class gmdm

Usage

## S3 method for class 'gmdm'
plot(x, labels, colors, type = NULL, main, ylab = "Fraction",
  xlab = "Position", label.length.max = 8, label.line.max = 3,
  cex.text = 1, cex.tickmark = 0.75, if.plot.new = TRUE, ...)

Arguments

x	an object of class gmdm.
labels	a string vector of the same length as x$data, giving the names of the numeric vectors in x$data.
colors	the colors of the discrete distributions; the default is "Dark2" colors in ColorBrewer palettes if not specified.
type	type of plot, as in help("plot", package="graphics").
main	an overall title for the plot. See help("title", package="graphics"); the default title is used if not specified.
ylab	a title for the y axis. See help("title", package="graphics").
xlab	a title for the x axis. See help("title", package="graphics").
label.length.max	numeric, giving the maximum string width allowed in diagonal labels.
label.line.max	numeric, giving the maximum number of lines allowed in diagonal labels.
cex.text	a numerical value giving the amount by which plot text should be magnified relative to the default.
cex.tickmark	a numerical value giving the amount by which tickmarks should be magnified relative to the default.
if.plot.new	logical, indicating whether to start a new plot device.
...	arguments to be passed to methods, see gmdp.

Details

S3 method for class gmdm

References

See help(GMD)

See Also

gmdm, gmdp

Examples

## ------------------------------------------------------------------------
## Example1: CAGE
## ------------------------------------------------------------------------
require("GMD") # load library
data(cage)     # load data

## construct a distance matrix and visualize it
short.labels <- gsub("(.+) \\(.+","\\1",names(cage)) # get short labels
x <- gmdm(cage[1:6],labels=short.labels[1:6])
plot(x)

## Not run: 
## ------------------------------------------------------------------------
## Example2: ChIP-seq
## ------------------------------------------------------------------------
data(chipseq_mES)   # load data
data(chipseq_hCD4T) # load data

## pairwise distance and alignment based on GMD metric
plot(gmdm(chipseq_mES,sliding=FALSE))

## clustering on spatial distributions of histone modifications
x <- gmdm(chipseq_hCD4T,sliding=FALSE,resolution=10)
heatmap.3(x,revC=TRUE)

## End(Not run)

GMD documentation built on May 29, 2017, 10:41 a.m.