| BinnedScatter {aroma.core} | R Documentation |
Package: aroma.core
Class BinnedScatter
list
~~|
~~+--BinnedScatter
Directly known subclasses:
public class BinnedScatter
extends list
BinnedScatter(data=NULL, density=NULL, map=NULL, params=NULL)
data |
A Nx2 @numaric matrix. |
density |
... |
map |
... |
params |
A list of parameters. |
... |
Not used. |
Methods:
plot | - | |
points | - | |
reorder | - | |
subsample | - | |
subset | - |
Methods inherited from list:
all.equal, as.data.frame, attachLocally, averageQuantile, callHooks, listToXml, mergeBoxplotStats, normalizeAverage, normalizeQuantileRank, normalizeQuantileSpline, plotDensity, relist, within
Henrik Bengtsson (http://www.braju.com/R/)
The spatial density is estimated by internal functions of the smoothScatter package.
# Sample scatter data
n <- 10e3
x <- rnorm(n=n)
y <- rnorm(n=n)
xy <- cbind(x=x, y=sin(x)+y/5)
# Bin data and estimate densities
xyd <- binScatter(xy)
layout(matrix(1:4, nrow=2))
par(mar=c(5,4,2,1))
# Plot data
plot(xyd, pch=1)
# Thin scatter data by subsampling
rhos <- c(1/3, 1/4, 1/6)
for (kk in seq(along=rhos)) {
xyd2 <- subsample(xyd, size=rhos[kk])
points(xyd2, pch=1, col=kk+1)
}
for (kk in seq(along=rhos)) {
xyd2 <- subsample(xyd, size=rhos[kk])
plot(xyd2, pch=1, col=kk+1)
mtext(side=3, line=0, sprintf("Density: %.1f%%", 100*rhos[kk]))
}