smoothScatter {graphics} | R Documentation |
smoothScatter
produces a smoothed color density
representation of the scatterplot, obtained through a kernel
density estimate. densCols
produces a vector containing
colors which encode the local densities at each point in a
scatterplot.
smoothScatter(x, y = NULL, nbin = 128, bandwidth, colramp = colorRampPalette(c("white", blues9)), nrpoints = 100, pch = ".", cex = 1, col = "black", transformation = function(x) x^.25, postPlotHook = box, xlab = NULL, ylab = NULL, xlim, ylim, xaxs = par("xaxs"), yaxs = par("yaxs"), ...)
x, y |
the |
nbin |
numeric vector of length one (for both directions) or two
(for x and y separately) specifying the number of equally spaced
grid points for the density estimation; directly used as
|
bandwidth |
numeric vector (length 1 or 2) of smoothing bandwidth(s).
If missing, a more or less useful default is used. |
colramp |
function accepting an integer |
nrpoints |
number of points to be superimposed on the density
image. The first |
pch, cex, col |
arguments passed to |
transformation |
function mapping the density scale to the color scale. |
postPlotHook |
either |
xlab, ylab |
character strings to be used as axis labels, passed
to |
xlim, ylim |
numeric vectors of length 2 specifying axis limits. |
xaxs, yaxs, ... |
further arguments, passed to |
smoothScatter
produces a smoothed version of a scatter plot.
Two dimensional (kernel density) smoothing is performed by
bkde2D
from package KernSmooth.
See the examples for how to use this function together with
pairs
.
Florian Hahne at FHCRC, originally
bkde2D
from package KernSmooth;
densCols
which uses the same smoothing computations and
blues9
in package grDevices.
scatter.smooth
adds a loess
regression smoother to a scatter plot.
## A largish data set n <- 10000 x1 <- matrix(rnorm(n), ncol = 2) x2 <- matrix(rnorm(n, mean = 3, sd = 1.5), ncol = 2) x <- rbind(x1, x2) oldpar <- par(mfrow = c(2, 2)) smoothScatter(x, nrpoints = 0) smoothScatter(x) ## a different color scheme: Lab.palette <- colorRampPalette(c("blue", "orange", "red"), space = "Lab") smoothScatter(x, colramp = Lab.palette) ## somewhat similar, using identical smoothing computations, ## but considerably *less* efficient for really large data: plot(x, col = densCols(x), pch = 20) ## use with pairs: par(mfrow = c(1, 1)) y <- matrix(rnorm(40000), ncol = 4) + 3*rnorm(10000) y[, c(2,4)] <- -y[, c(2,4)] pairs(y, panel = function(...) smoothScatter(..., nrpoints = 0, add = TRUE)) par(oldpar)