Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates the minimum-hypergeometric (mHG) statistic.
mHG definition: mHG(lambdas) = min over 1 <= n < N of HGT (b_n(lambdas); N, B, n)
Where HGT is the hypergeometric tail: HGT(b; N, B, n) = Probability(X >= b),
and b_n = sum over 1 <= i <= n of lambdas[i].
1 | mHG.statistic.calc(lambdas, n_max = length(lambdas))
|
lambdas |
\{0,1\}^N, sorted from top to bottom. |
n_max |
the algorithm will only consider the first n_max partitions. |
O(n_max+B^2*log(B)) running time, O(B) space.
Instance of the class mHG.statistic.info
(stores the statistics, and for which n and b_n it was obtained).
If several n give the same mHG, the smallest one is chosen.
Kobi Perl
Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa. Retrieved from http://bioinfo.cs.technion.ac.il/people/zohar/thesis/eran.pdf (pages 10-11, 18-19)
1 2 3 4 5 | N <- 50
B <- 15
lambdas <- numeric(50)
lambdas[sample(N, B)] <- 1
mHG.statistic.info <- mHG.statistic.calc(lambdas)@mHG
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