You can also specify weights in sm.density() in the package sm.
Cheers
Francisco
From: Prof Brian Ripley [EMAIL PROTECTED]
To: [EMAIL PROTECTED]
CC: r-help@stat.math.ethz.ch
Subject: Re: [R] kernel smoothing of weighted data
Date: Tue, 16 Aug 2005 18:13:43 +0100 (BST)
density() in the R-devel version of R allows weights.
locfit() in the package of the same name also appears to be documented to.
On Tue, 16 Aug 2005 [EMAIL PROTECTED] wrote:
I want to use kde() or a similar function for kernel smoothing but I
want
to specify the weight of each of my data points. I do not want to
specify
the bandwidth on a point by point basis.
The only kde() I found is from the recent package ks, and is for
multivariate data -- if you want that, you did not say so and I've not
looked for an answer there.
This seems such a simple and obvious thing to want to do I am suspicious
that there is not an obvious way to do it. The only discussion I have
found is about negative weights(!) and says nothing about
implementation.
Can anyone suggest a package I have missed or suggest the best starting
point for writing my own solution.
The reason for wanting this is that I have a number of samples each of
~1000 data points from the same distribution but the samples are of
slightly differing statistical weight and eventually each point in each
sample may have its own statistical weight.
I have searched the list but I am not subscribed to it so please make me
an
addressee of any reply.
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UKFax: +44 1865 272595
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