fn() calculateCovarianceCalculates the covariance for the number of word occurrences for two words in a sequence of length n, given a
background model.
Defined in  <seqan/alignment_free.h> 

Signature 
void calculateCovariance(covariance, word1, word2, bgFrequencies, n);
void calculateCovariance(covariance, word1, word2, bgModel, n);

Parameters
covariance

Variance of the number of occurrences of the word in a sequence of length n given the model, double. 

word1

String, usually of Dna. 
word2

String, usually of Dna. 
bgFrequencies

String of double with the background frequencies representing 
bgModel

MarkovModel to use. 
n

Length of the sequence where the occurrences of word are counted, int. 
Detailed Description
Calculates the covariance for the number of word occurrences for two words in a sequence of length n given a background model (Markov model or Bernoulli model). The covariance is influenced by the property of words to overlap, for example, the words ATAT and TATA have a high covariance since they are likely to overlap. The formula is based on (Robin et al., 2005).
References
Robin, S., Rodolphe, F., and Schbath, S. (2005). DNA, Words and Models. Cambridge University Press. See Jonathan Goeke et al (to appear) for details on the implementation.
Examples
Calculate the covariance for the number of occurrences of ATATAT and TATATA in a sequence of length 10000bp with p(A) = p(T) = 0.3 and p(C) = p(G) = 0.2.
using namespace seqan; double covar = 0.0; int n = 10000; DnaString word1 = "ATATAT"; DnaString word2 = "TATATA"; String<double> model; resize(model, 4); model[0] = 0.3; // p(A) model[1] = 0.2; // p(C) model[2] = 0.2; // p(G) model[3] = 0.3; // p(T) calculateCovariance(covar, word1, word2, model, n); // covar = 4.74
Estimate a Markov model on a set of sequences and calculate the covariance for the number of occurrences of ATATAT and TATATA in a sequence of length 10000bp.
using namespace seqan; double covar = 0.0; int n = 10000; DnaString word1 = "ATATAT"; DnaString word2 = "TATATA"; StringSet<DnaString> sequences; appendValue(sequences, "CAGCACTGATTAACAGGAATAAGCAGTTTACTTCTGTCAGAATATTGGGCATATATA" "CTGGGACCCGTGTAATACTCTAATTTAATTAGGTGATCCCTGCGAAGTCTCCA"); MarkovModel<Dna, double> modelMM0(0); // Bernoulli model modelMM0.build(sequences); calculateCovariance(covar, word1, word2, modelMM0, n); // covar = 4.74 MarkovModel<Dna, double> modelMM1(1); // First order Markov model modelMM1.build(sequences); calculateCovariance(covar, word1, word2, modelMM1, n); // covar = 13.1541