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, 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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