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,

João Fadista
Ph.d. student


 	
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