On Jul 12, 2009, at 3:21 PM, maram salem wrote:
Dear group,
Thank u so much 4 ur help. I've tried the link,
http://finzi.psych.upenn.edu/R/library/quantreg/html/akj.html
for adaptive kernel density estimation.
But since I'm an R beginer and the topic of adaptive estimation is
new for me, i still can't figure out some of the arguments of
akj(x, z =, p =, h = -1, alpha = 0.5, kappa = 0.9, iker1 = 0)
I've a vector of 1000 values (my X), but I don't know how to get the Z
That does seem rather trivial. According to the help page, those are
just the points at which the density should be estimated. The example
in the help page shows you how to create a suitable vector.
and what's Kappa?
Not so obvious. Experimentation shows that reducing kappa makes the
estimates less smooth.
I'm sorry if the question is trivial but I hope u could recommend
some refrence if u know one.
Koenker gives two references and apparently you have some other
material you are reading. Your university should have access to the
Project Euclid Annals of Statistics copies that are found with the
obvious Google search strategy. Maybe you should be questioning the
overall strategy of using a function you don't understand. Why, for
instance, do you even have an interest in this function?
Thank u so much again
Maram
________________________________
From: John Kane <jrkrid...@yahoo.ca>
Sent: Monday, June 29, 2009 10:35:49 PM
Subject: Re: [R] (no subject)
Perhaps?
http://finzi.psych.upenn.edu/R/library/quantreg/html/akj.html
Subject: [R] (no subject)
To: r-help@r-project.org
Received: Monday, June 29, 2009, 9:05 AM
Hi group,
I found a module for adaptive kernel density estimation for
Stata users, but unfortunetly I don't have access to Stata,
can I find a similar approach using R?
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
______________________________________________
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.
