divergence.Shannon: Divergencia.Shannon: Mutual Information
In MEET: MEET: Motif Elements Estimation Toolkit

Description Usage Arguments Details Author(s) References See Also Examples

This function calculates Mutual Information (Renyi Order equal 1) by means of Kullback-Leibler divergence

1	divergence.Shannon(training.set, H, HXY,correction)

`training.set`	A set of aligned nucleotide sequences
`H`	Entropy
`HXY`	Joint Entropy
`correction`	Correction of the Finite Sample Size Effect

Renyi Order has to be equal 1.

Joan Maynou <joan.maynou@upc.edu>

J. Maynou, M. Vallverdu, F. Claria, J.J. Gallardo-Chacon, P. Caminal and A. Perera, Transcription Factor Binding Site Detection through Position Cross-Mutual Information variability analysis. 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

divergence.Renyi, PredictDivergence, kfold.divergence

data(BackgroundOrganism)
data(iicc)
HXmax<-iicc$HXmax
H<-iicc$Entropy[[1]]
q<-1
training.set<-iicc$Transcriptionfactor
correction<-correction.entropy(q,p=nrow(training.set),long=1,iicc)
pmX<-probability(training.set,Prob,missing.fun=TRUE)
prob_parella<-probability.couple(Prob)
pmXY<-joint.probability(training.set, Prob, prob_parella)
HXY<-entropy.joint(pmXY,q,iicc)
divergence.Shannon(training.set,H,HXY,correction)