Trafim Vanishek posted a similar problem: "Joint density approximation?"
(without any solution for kde2d). Here is an example to illustrate my
problem.
Originally data is for example:
a=runif(10) (yes, the number of data should be larger)
b=runif(10)
c=kde2d(a,b,n=10,lims=c(0,1,0,1))
attach(c)
The equally spaced vectors x and y returned are:
x = [1] 0.0000000 0.1111111 0.2222222
0.3333333 0.4444444 0.5555556 0.6666667
[8] 0.7777778 0.8888889 1.0000000
y = [1] 0.0000000 0.1111111 0.2222222
0.3333333 0.4444444 0.5555556 0.6666667
[8] 0.7777778 0.8888889 1.0000000
and the corresponding density estimates for any combination of these
coordinates is a 10 x 10 matrix z.
I want the density estimates for the points in a k x 2 matrix like for
example
A=[(0,7,0.3),(0.1,0.2),...,(0.5,0.9)]^T
which is not equally spaced, (and i do not need the density of every
combination of these coordinates). So that the output is f(0.7,0.3),
f(0.1,0.2), ... ,f(0.5,0.9), where "f" is the kernel estimator based on a
and b.
Geir
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