| BinnedScatter | 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  | 
| density | ... | 
| map | ... | 
| params | A  | 
Methods:
| plot | - | |
| points | - | |
| reorder | - | |
| subsample | - | |
| subset | - | |
Methods inherited from list:
Ops,nonStructure,vector-method, Ops,structure,vector-method, Ops,vector,nonStructure-method, Ops,vector,structure-method, all.equal, as.CopyNumberDataSetTuple, as.data.frame, attachLocally, callHooks, coerce,ANY,list-method, exportAromaUnitPscnBinarySet, listToXml, mergeBoxplotStats, relist, reset, type.convert, within
Henrik Bengtsson
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]))
}
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