Description Usage Arguments Details Value Author(s) See Also Examples
AIC
1 |
seq |
List of sequence(s) |
E |
Vector of state space |
mu |
Vector of initial distribution |
Ptrans |
Matrix of transition probabilities |
k |
Order of Markov model |
AIC(M) = -2*log{L} + 2*M, where L is the log-likelihood, M is the number of parameters of the model.
AIC |
List: value of AIC for each sequence |
Vlad Stefan Barbu, barbu@univ-rouen.fr
Caroline Berard, caroline.berard@univ-rouen.fr
Dominique Cellier, dominique.cellier@laposte.net
Mathilde Sautreuil, mathilde.sautreuil@etu.univ-rouen.fr
Nicolas Vergne, nicolas.vergne@univ-rouen.fr
simulSM, estimMk, simulMk, estimSM, LoglikelihoodSM, LoglikelihoodMk
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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 AIC
#################################
AIC_Mk(seq = seq2, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, k = 1)
#[[1]]
#[1] 60.20263
#
#[[2]]
#[1] 115.7674
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