Mathematica SmoothKernelDistribution equivalent in R

Question

@vucko gave me excellent answer on my question, unfortunately using Mathematica code. I'm trying to rewrite it in R and I'm lost in R functions providing kernel density estimation.

I have bivariate dataset with more than 46,000 rows (so I am also looking for a high performance solution--@vucko's solution is very time consuming). I would like to apply kernel density estimation and decide if some point lies in area with some density estimation level (confidence level respectively).

@vucko in his answer selected two groups. I need only to know if some point lies in the green group or not. And that should be done with R.

I experimented with kde and bkde2D functions but they don't provide me desired functionality as Mathematica SmoothKernelDistribution. Can you please me show the direction? For normal distribution I found the ellipse function which approximated data with some confidence level and used inside.owin function.

Answer 1

I think the hdrcde package does what you want. Here is something quite similar to your example:

require(hdrcde)
n <- 23000
x <- c(runif(n,0,1),runif(n,0,.6))
y <- c(x[1:n], 7*x[n+(1:n)]) + rnorm(n)
y <- (y-min(y))/(max(y)-min(y))
plot(x,y)

den <- hdr.boxplot.2d(x,y,prob=.30,h=c(2.,2),pch=".",pointcol="red")
j <- (den$fxy > den$falpha)
points(x[j],y[j],col="green",pch=".")

That will give you the points within the contour of 30% probability. It is very fast, even with 46000 observations.