I am trying to visualize the surface of a bivariate kernel density
estimate. I have a vector of bivariate observations(x,y), and a function which
computes the kernel density estimate z corresponding to each
observation. I cannot generate the (x,y) data in the ascending order
needed by persp(x,y,z).
That's not a very useful kernel density estimate function. The kernel2d function in library(splancs) takes some x,y points, a kernel width, and a number of grid point in x and y and returns something that you can feed into persp() or image(). Can't you edit your kernel function to give you values away from the observations?
I was wondering whether there is an R version of the S function interp.
A simple visualisation can be acheived by plotting the points with a symbol size related to the value at the point. e.g.
xyz=data.frame(x=runif(10),y=runif(10),z=runif(10)^2) plot(xyz$x,xyz$y,cex=xyz$z*3)
You'll have to make sure the cex parameter is scaled to a range that looks good on your graphics device, which is where the '*3' multiplier comes from in my example. Normal points have cex=1. Points with cex=0 are invisible to you may want to add a small offset.
Barry
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