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smoothScatter: Scatterplots with Smoothed Densities Color Representation

smoothScatter

R Documentation

Scatterplots with Smoothed Densities Color Representation

Description

smoothScatter produces a smoothed color density representation of a scatterplot, obtained through a (2D) kernel density estimate.

Usage

smoothScatter(x, y = NULL, nbin = 128, bandwidth,
              colramp = colorRampPalette(c("white", blues9)),
              nrpoints = 100, ret.selection = FALSE,
              pch = ".", cex = 1, col = "black",
              transformation = function(x) x^.25,
              postPlotHook = box,
              xlab = NULL, ylab = NULL, xlim, ylim,
              xaxs = par("xaxs"), yaxs = par("yaxs"), ...)

Arguments

`x, y`	the `x` and `y` arguments provide the x and y coordinates for the plot. Any reasonable way of defining the coordinates is acceptable. See the function `xy.coords` for details. If supplied separately, they must be of the same length.
`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 `gridsize` in `bkde2D()`.
`bandwidth`	numeric vector (length 1 or 2) of smoothing bandwidth(s). If missing, a more or less useful default is used. `bandwidth` is subsequently passed to function `bkde2D`.
`colramp`	function accepting an integer `n` as an argument and returning `n` colors.
`nrpoints`	number of points to be superimposed on the density image. The first `nrpoints` points from those areas of lowest regional densities will be plotted. Adding points to the plot allows for the identification of outliers. If all points are to be plotted, choose `nrpoints = Inf`.
`ret.selection`	`logical` indicating to return the ordered indices of “low density” points if `nrpoints > 0`.
`pch, cex, col`	arguments passed to `points`, when `nrpoints > 0`: point symbol, character expansion factor and color, see also `par`.
`transformation`	function mapping the density scale to the color scale.
`postPlotHook`	either `NULL` or a function which will be called (with no arguments) after `image`.
`xlab, ylab`	character strings to be used as axis labels, passed to `image`.
`xlim, ylim`	numeric vectors of length 2 specifying axis limits.
`xaxs, yaxs, ...`	further arguments passed to `image`, e.g., `add=TRUE` or `useRaster=TRUE`.

Details

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.

Value

If ret.selection is true, a vector of integers of length nrpoints (or smaller, if there are less finite points inside xlim and ylim) with the indices of the low-density points drawn, ordered with lowest density first.

Author(s)

Florian Hahne at FHCRC, originally

Examples

## 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), mar=.1+c(3,3,1,1), mgp = c(1.5, 0.5, 0))
smoothScatter(x, nrpoints = 0)
smoothScatter(x)

## a different color scheme:
Lab.palette <- colorRampPalette(c("blue", "orange", "red"), space = "Lab")
i.s <- smoothScatter(x, colramp = Lab.palette,
                     ## pch=NA: do not draw them
                     nrpoints = 250, ret.selection=TRUE)
## label the 20 very lowest-density points,the "outliers" (with obs.number):
i.20 <- i.s[1:20]
text(x[i.20,], labels = i.20, cex= 0.75)

## 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),
      gap = 0.2)

par(oldpar)