I am afraid your notion of a "concrete idea" is less concrete than
what I would need to understand what you are requesting. Your first
lines of example code should be:
library(<if the density function is not from stats>)
<sample data construction of x>
density(x, <some set of parameters>)
... since stats::density() by default would return 512 y estimates,
even if the length of x were longer.
> x <- rnorm(3471)
> plot(density(x))
> str(density(x))
List of 7
$ x : num [1:512] -3.98 -3.96 -3.94 -3.93 -3.91 ...
$ y : num [1:512] 7.98e-06
--
David Winsemius
On Mar 25, 2009, at 9:30 AM, Pradeep Raje wrote:
Dear all:Request your indulgence. The econophysics gurus do this
stuff all
the time: all their PDFs are smooth, with neat log x axis.
1. The kernel density estimate (KDE) function returns the empirical
probability density at 2^n points (min: 512). The big question is
how do I
scale back the x-values (say, density$x) to x-values in terms of the
original dataset?
2. To give you a concrete idea, i have a dataset of 3471 obs (x=date
index,
y=parameter values). Now the density estimate d<-density(x) gives be
2048
x-values. When I plot the PDF, the x axis is obviously d$x,
length=2048.
3. How can I scale back these 2048 values to get a sense of calendar
time
(original date index)?
4. Subsidiary question is: how do i bring in the remaining values
(3471-2048)?
You seem to have the idea that the original data is "lined up" with
the density estimates. That is not so.
Thanks very much in advance.
pradeep
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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