On Jul 12, 2009, at 2:56 PM, David Winsemius wrote:
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?
The Silverman book on density estimation is as close to a canonical
reference as one is
likely to encounter in statistics, the akj() function implements
Silverman's adaptive kernel
proposal so it would be quite helpful to have this reference at hand.
That is why it was
cited in the man page for the function.
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