On Fri, 23 Jun 2006, Rogério Rosa da Silva wrote:
Dear All,
My doubt about how to integrate a simple kernel density estimation goes on.
I have seen the recent posts on integrate density estimation, which seem
similar to my question. However, I haven't found a solution.
I have made two simple kernel density estimation by:
kde.1 <-density(x, bw=sd(x), kernel="gaussian")$y # x<- c(2,3,5,12)
kde.2 <-density(y, bw=sd(y), kernel="gaussian")$y # y<- c(4,2,4,11)
Now I would like to integrate the difference in the estimated density
values, i.e.:
diff.kde <- abs (kde.1- kde.2)
How can I integrate diff.kde over -Inf to Inf ?
Well, the answer is zero.
Computationally this is a bit tricky. You can turn the density estimates
into functions with approxfun()
x<-rexp(100)
kde<-density(x)
f<-approxfun(kde$x,kde$y,rule=2)
integrate(f,-1,10)
1.000936 with absolute error < 3.3e-05
But if you want to integrate over -Inf to Inf you need the function to
specify the values outside the range of the data. The only value that
will work over the range -Inf to Inf is zero
f<-approxfun(kde$x,kde$y,yleft=0,yright=0)
integrate(f,-1,10)
1.00072 with absolute error < 1.5e-05
integrate(f,-Inf,Inf)
1.000811 with absolute error < 2.3e-05
-thomas______________________________________________
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