On Aug 22, 2009, at 9:44 AM, maram salem wrote:
Dear All,
I have a variable q which is a vector of 1000 simulated positive
values; that is I generated 1000 samples from the pareto
distribution, from each sample I calculated the value of q ( a
certain fn in the sample observations), and thus I was left with
1000 values of q and I don't know the distribution of q.
Hence, I used the given code for kernel density estimation to
estimate the density of q
>options(scipen=4)
d <- density(q, bw = "nrd0",kernel="gaussian")
d
plot(d)
But what I'm really intersed in is to estimate the probability that
q is greater than a certain value , for ex.,P(q>11000), using the
kernel density estimate I obtained.
Could u help me with a fn or some document to do this?
Thank u so much
I do not understand why you think that creating a density estimate is
needed or even useful for the purpose. Why would you not simply
compute the Pr(q > 11000) on the original sample?
--
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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