On Sun, 19 Jul 2009, Hadassa Brunschwig wrote:
Hi
I am not sure what you mean by sampling an index of a group of
intervals. I will try to give an example:
Let's assume I have a vector 1:1000000. Let's say I have 10 intervals
of different but known length, say,
c(4,6,11,2,8,14,7,2,18,32). For simulation purposes I have to sample
those 10 intervals 1000 times.
The requirement is, however, that they should be of those lengths and
should not be overlapping.
In short, I would like to obtain a 10x1000 matrix with sampled intervals.
Something like this:
lens <- c(4,6,11,2,8,14,7,2,18,32)
perm.lens <- sample(lens)
sort(sample(1e06-sum(lens)+length(lens),length(lens)))+cumsum(c(0,head(perm.lens,-1)))
[1] 15424 261927 430276 445976 451069 546578 656123 890494 939714 969643
The vector above gives the starting points for the intervals whose lengths
are perm.lens.
I'd bet every introductory combinatorics book has a problem or example in
which the expression for the number of ways in which K ordered objects can
be assigned to I groups consisting of n_i adjacent objects each is
constructed. The construction is along the lines of the calculation above.
HTH,
Chuck
Thanks
Hadassa
On Sun, Jul 19, 2009 at 9:48 PM, David Winsemius<dwinsem...@comcast.net> wrote:
On Jul 19, 2009, at 1:05 PM, Hadassa Brunschwig wrote:
Hi,
I hope I am not repeating a question which has been posed already.
I am trying to do the following in the most efficient way:
I would like to sample from a finite (large) set of integers n
non-overlapping
intervals, where each interval i has a different, set length L_i
(which is the number
of integers in the interval).
I had the idea to sample recursively on a vector with the already
chosen intervals
discarded but that seems to be too complicated.
It might be ridiculously easy if you sampled on an index of a group of
intervals.
Why not pose the question in the form of example data.frames or other
classes of R objects? Specification of the desired output would be
essential. I think further specification of the sampling strategy would also
help because I am unable to understand what sort of probability model you
are hoping to apply.
Any suggestions on that?
Thanks a lot.
Hadassa
--
Hadassa Brunschwig
PhD Student
Department of Statistics
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
--
Hadassa Brunschwig
PhD Student
Department of Statistics
The Hebrew University of Jerusalem
http://www.stat.huji.ac.il
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