LoglikelihoodMk {SMM}

Loglikelihood (Markov model)

Description

Computation of the loglikelihood starting from sequence(s), alphabet, initial distribution, transition matrix

Usage

LoglikelihoodMk(seq, E, mu, Ptrans, k)

Arguments

seq

List of sequence(s)

E

Vector of state space

mu

Vector of initial distribution

Ptrans

Matrix of transition probabilities

k

Order of the Markov chain

Value

L

Value of loglikelihood for each sequence

Author(s)

Vlad Stefan Barbu, [e-mail]
Caroline Berard, [e-mail]
Dominique Cellier, [e-mail]
Mathilde Sautreuil, [e-mail]
Nicolas Vergne, [e-mail]

See Also

simulSM, estimMk, simulMk, estimSM, LoglikelihoodSM

Examples

alphabet = c("a","c","g","t")
S = length(alphabet)
# creation of the transition matrix
Pij = matrix(c(0,0.2,0.3,0.5,0.4,0,0.2,0.4,0.1,0.2,0,0.7,0.8,0.1,0.1,0), 
nrow = S, ncol = S, byrow = TRUE)

#Pij
#     [,1] [,2] [,3] [,4]
#[1,]  0.0  0.2  0.3  0.5
#[2,]  0.4  0.0  0.2  0.4
#[3,]  0.1  0.2  0.0  0.7
#[4,]  0.8  0.1  0.1  0.0


## Simulation of two sequences of length 20 and 50 respectively
seq2 = simulMk(E = alphabet, nbSeq = 2, lengthSeq = c(20,50), Ptrans = Pij, 
init = rep(1/4,4), k = 1)
                
####################################
## Computation of the loglikelihood
####################################
LoglikelihoodMk(seq = seq2, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, k = 1)

#$L
#$L[[1]]
#[1] -13.90161
#
#$L[[2]]
#[1] -39.58438