Behalf Of Justine Rochon
> > Sent: Thursday, November 05, 2009 2:38 AM
> > To: r-help@r-project.org
> > Subject: [R] Density estimate with bounds
> >
> > Dear R users,
> >
> > I would like to show the estimated density of a (0, 1) uniformly
> > distributed ra
Edna Bell gmail.com> writes:
> I have a tiny data set:
> > xs
> [1] 0.7 2.8 0.1 1.9 0.0 1.4 0.2 2.3 0.3 0.2
> >
>
> and generate the density function for it.
>
> I would like to replicate this. Is there a straightforward way to do
> this, please?
See chapter 5 in MASS and the examples in lib
Hi R Gurus:
I have a tiny data set:
> xs
[1] 0.7 2.8 0.1 1.9 0.0 1.4 0.2 2.3 0.3 0.2
>
and generate the density function for it.
I would like to replicate this. Is there a straightforward way to do
this, please?
According to the help file, the FFT is used.
How does this compare to Silverman'
robability
function for continuous distributions?
Thaks,
Lavan
--- On Mon, 9/29/08, Eik Vettorazzi <[EMAIL PROTECTED]> wrote:
From: Eik Vettorazzi <[EMAIL PROTECTED]>
Subject: Re: [R] density estimate
To: "Lavan" <[EMAIL PROTECTED]>
Cc: r-help@r-project.org
Date: Monday
Hi Lavan,
a continuous density is not restricted to be within [0, 1]. Its only
bound to have an integral of 1.
For example
dnorm(0,sd=.1)
is a very common density and gives 3.989423. A density function is not a
probability function!
If you think your data x is discrete than you can assign the c
Hi,
I have a vector or random variables and I'm estimating the density using
"bkde" function in the KernSmooth package. The out put contains two vectors
(x and y), and the R documentation calls y as the density estimates, but my
y-values are not exact density etstimates (since these are numbers l
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