May be a kludge, but it might be simpler to write your own density function for
a few specified points.
For example
my.density <- function(x, bw = 'nrd0', at) {
x<-na.omit(x)
#####
#Borrowed from density.default for compatibility
if (is.character(bw)) {
if (length(x) < 2)
stop("need at least 2 points to select a bandwidth automatically")
bw <- switch(tolower(bw), nrd0 = bw.nrd0(x), nrd = bw.nrd(x),
ucv = bw.ucv(x), bcv = bw.bcv(x), sj = , `sj-ste` = bw.SJ(x,
method = "ste"), `sj-dpi` = bw.SJ(x, method = "dpi"),
stop("unknown bandwidth rule"))
}
######
at <- matrix(at, ncol=1)
y <- apply(at, 1, FUN=function(a, x, bw) sum(dnorm(a, x, bw)/length(x)),
x=x, bw=bw )
return(list(x=at, y=y))
}
x<-rnorm(500)
plot(density(x))
at<-seq(-2, 2, 0.25)
points(my.density(x, at=at))
> -----Original Message-----
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of Carsten Harlaß
> Sent: 27 June 2011 20:19
> To: dcarl...@tamu.edu
> Cc: r-help@r-project.org
> Subject: Re: [R] Kernel Density Estimation at manually
> specified points
>
> Hello,
>
> thanks for your response.
>
> Of course I already thought about this "simple" solution of
> the problem.
> But I think this is not a nice workaround.
>
> If I understand the manual correctly, density() already makes
> an approximation. But this approximation is just evaluated at
> the wrong points.
>
> See:
>
> "Details
>
> The algorithm used in density.default disperses the mass of
> the empirical distribution function over a regular grid of at
> least 512 points and then uses the fast Fourier transform to
> convolve this approximation with a discretized version of the
> kernel and then uses linear approximation to evaluate the
> density at the specified points."
>
> and
>
> "n the number of equally spaced points at which the
> density is to be
> estimated. When n > 512, it is rounded up to a power of 2
> during the calculations (as fft is used) and the final result
> is interpolated by approx. So it almost always makes sense to
> specify n as a power of two. "
>
> So this workaround means putting a second approximation on
> top of an other approximation. That's not nice. Or is it?
>
> With kind regards
>
> Carsten
>
> Am 27.06.2011 19:16, schrieb David L Carlson:
> > Look at ?approx. For your example (of course your random
> numbers give
> > different results):
> >
> >> approx(f$x, f$y, c(-2, -1, 0, 1, 2))
> > $x
> > [1] -2 -1 0 1 2
> >
> > $y
> > [1] 0.03757113 0.19007982 0.31941779 0.37066592 0.10227509
> >
> > approx gives NA's if you try to interpolate outside the
> bounds of the data.
> > ----------------------------------------------
> > David L Carlson
> > Associate Professor of Anthropology
> > Texas A&M University
> > College Station, TX 77843-4352
> >
> >
> > -----Original Message-----
> > From: r-help-boun...@r-project.org
> > [mailto:r-help-boun...@r-project.org] On Behalf Of Carsten Harlaß
> > Sent: Sunday, June 26, 2011 7:02 PM
> > To: r-help@r-project.org
> > Subject: [R] Kernel Density Estimation at manually specified points
> >
> > Hello,
> >
> > my name is Carsten. This ist my first post to R-help mailing list.
> >
> > I estimate densities with the function "density" out of the package
> > "stats".
> >
> > A simplified example:
> >
> >
> > #generation of test data
> > n=10
> > z = rnorm(n)
> >
> > #density estimation
> > f=density(z,kernel="epanechnikov",n=n)
> >
> > #evaluation
> > print(f$y[5])
> >
> > Here I can only evaluate the estimation at given points.
> These points
> > are determined by the parameter n. By default they are equidistant
> > distributed on the interesting interval.
> >
> > But I need to evaluate the estimation (the estimated densitiy
> > function) at manually specified points. For example I want
> to compute f(z[i]).
> > This means I am interested in the estimated density at a the
> > observation z[i].
> >
> > Does anyone know how I can compute this? I think this is an
> ordinary
> > task so I would be surprised if R can not manage this. But
> even after
> > a long search I have found nothing.
> >
> > Thanks in advance
> >
> > Carsten Harlaß
> >
> > --
> > Carsten Harlaß
> > Aachen University of Applied Sciences
> > Campus Jülich
> > E-Mail: carsten_harl...@web.de
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
>
> > --
> > Carsten Harlass
> > Aachen University of Applied Sciences
> > Campus Juelich
> > E-Mail: carsten_harl...@web.de
>
> ______________________________________________
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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