# [R] distribution of peaks in random data results

Date: Thu, 31 May 2007 17:17:28 +0200

Dear all,

I have the positions of N points spread through some sequence of length L (L>N), and I would like to know how can do the following:

1- Permute the positions of the N points along the whole sequence. Assuming a uniform distribution I did: position1 <- runif(N, 1, L)

2- Apply a kernel convolution method to the resulting permuted points profile. For this I applied the function: d <- density(position1, bw = "sj")

3- Record the heights of all peaks.
For this I used the estimated density values from the output of the density function above: heights1 <- d\$y

4- Repeat step 1 and 2 to be able to have a distribution of the peaks from the random data results. I don“t know how to perform this step!!!

5- Compute the threshold by determining the alfa-level in the empirical CDF of the null distribution. Assuming “heightsALL“ is the output of step 4 I would do this: plot(ecdf(heightsALL)). But I don“t know how to compute the threshold

6- Apply this threshold to the peaks estimate of the real peaks data, resulting in a series of significant peaks. This step can be done by seeing the peaks in the real data that are above the threshold and classify these as significant at the alfa-level.

The steps mentioned above are better illustrated with a picture that can be fetched here: http://www.yousendit.com/transfer.php?action=download&ufid=0E3724F26CA53367

Best regards and thanks in advance,

Ph.d. student

UNIVERSITY OF AARHUS
Faculty of Agricultural Sciences
Dept. of Genetics and Biotechnology
Blichers Allé 20, P.O. BOX 50
DK-8830 Tjele

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