BioPhysConnectoR (version 1.6-10)

get.mie: Mutual Information

Description

The joint information content (mutual information) for an alignment is computed. Considering the gap problem there are four ways to compute it.

Usage

get.mie(aln, method = "ORMI", gapchar = NULL, nullmod = NULL, logMI = FALSE)

Arguments

aln

matrix of which the mutual information will be computed

method

method, that is used for the computation (see details)

gapchar

symbols of the input matrix that should be handled as gaps. These symbols are omitted in the computations (see details)

nullmod

integer specifying how many shuffle runs should be performed

logMI

boolean, if TRUE the log(MI) will be calculated, default is FALSE

Value

• Return value is the MI matrix per default. If a null model should be computed the returned value is a list of matrices.
• $mi
  MI matrix
• $nullmodel
  MI matrix of the null model
• $nullsquare
  matrix of the averaged MI squared values for the null model
• $nullvar
  matrix of the variance of each MI value over the whole shuffle run for the null model

Details

Methods: The mutual information (MI) is computed as follows: $$I(X,Y)=\sum_{x\in X}\sum_{y\in Y} p(x,y)\cdot\log_2\left(\frac{p(x,y)}{p(x)\cdot p(y)}\right)$$

$$I(X,Y)=H(X)+H(Y)-H(X,Y)$$ [object Object],[object Object],[object Object],[object Object]

See Also

get.entropy, get.entropy2p, freq1p, freq2p

Examples

seqa<-unlist(strsplit("PQITLWQRPLVTIKIGGQL",split=""))
seqb<-unlist(strsplit("PQITLWKRPLVTIRIGGQL",split=""))
seqc<-unlist(strsplit("PQITLWQRPLVTIKIGGQL",split=""))
a<-matrix(c(seqa,seqb,seqc),nrow=3,byrow=TRUE)
mi<-get.mie(a)
mi2<-get.mie(a, method = "SUMI", gapchar = "-")
mi_null<-get.mie(a,nullmod=100)
mi2_null<-get.mie(a, method = "SUMI", gapchar = "-",nullmod=100)