Pradeep Raje gmail.com> writes:
>
> Thanks David for your response.I had done that.
But not shared it.
> Problem is not with the
> computations, but in the interpretation.
> Assume that x ordinates are 'time' [1:3472], and y are associated parameter
> values.
> Now density gives me 512/1024/2
Thanks David for your response.I had done that. Problem is not with the
computations, but in the interpretation.
Assume that x ordinates are 'time' [1:3472], and y are associated parameter
values.
Now density gives me 512/1024/2048 x-ordinates, of which some (7 to be
precise) are negative. What do
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()
density(x, )
... since stats::density() by default would return 512 y estimates,
even if the length of x we
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-value
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