estimMk {SMM} | R Documentation |
Estimation of a k-th order Markov chain
Description
Estimation of the transition matrix and initial law of a k-th order Markov chain starting from one or several sequences.
Usage
estimMk(file = NULL, seq, E, k)
estimMk(file, seq = NULL, E, k)
Arguments
file |
Path of the file in fasta format which contains the sequences from which to estimate |
seq |
List of sequence(s) |
E |
Vector of state space |
k |
Order of the Markov chain |
Details
Let be a trajectory of length
of the Markov chain
of order k=1 with transition matrix
The estimation of the transition matrix is
where
is the number of transitions from state i to state j and
is the number of transition from state i to any state. For k > 1 we have similar expressions.
The initial distribution of a k-th order Markov chain is defined as
An estimation of the initial law for a first order Markov chain is assumed to be the estimation of the stationary law.
If the order of the Markov is greater than 1, then an estimation of the initial law is
, where
is the number occurences of state i in the sequences and
is the sum of the sequence lengths.
Value
estimMk returns the transition probability matrix of size (S^k)xS (with S = length(E)) and the initial law of size S estimated from the sequence(s) with a Markov model of order k.
The transition matrix is always given in the alphabetical and numerical order, even if the vector of state space is not given in this order.
Author(s)
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
See Also
simulMk, estimSM, simulSM
Examples
### Example 1 ###
# Second order model on the state space {a,c,g,t}
E <- c("a","c","g","t")
S = length(E)
init.distribution <- c(1/6,1/6,5/12,3/12)
k<-2
p <- matrix(0.25, nrow = S^k, ncol = S)
## simulation of 3 sequences with the simulMk function
seq3 = simulMk(E = E, nbSeq = 3, lengthSeq = c(1000, 10000, 2000), Ptrans = p,
init = init.distribution, k = 2)
## estimation of simulated sequences
res3 = estimMk(seq = seq3, E = E, k = 2)
## results of estimation
# initial law
res3$init
# [1] 0.2469048 0.2573333 0.2483810 0.2473810
# transition matrix
res3$Ptrans
# [,1] [,2] [,3] [,4]
# [1,] 0.2690616 0.2338710 0.2602639 0.2368035
# [2,] 0.2507553 0.2673716 0.2651057 0.2167674
# [3,] 0.2517758 0.2533544 0.2588792 0.2367798
# [4,] 0.2522376 0.2432872 0.2481692 0.2563059
# [5,] 0.2501949 0.2595479 0.2595479 0.2307093
# [6,] 0.2492775 0.2492775 0.2586705 0.2427746
# [7,] 0.2337662 0.2792208 0.2438672 0.2445887
# [8,] 0.2381306 0.2833828 0.2292285 0.2492582
# [9,] 0.2462745 0.2627451 0.2384314 0.2525490
#[10,] 0.2259760 0.2530030 0.2424925 0.2785285
#[11,] 0.2469512 0.2423780 0.2599085 0.2507622
#[12,] 0.2318393 0.2673879 0.2403400 0.2604328
#[13,] 0.2866192 0.2668250 0.2185273 0.2280285
#[14,] 0.2237711 0.2553191 0.2611886 0.2597212
#[15,] 0.2465863 0.2465863 0.2441767 0.2626506
#[16,] 0.2511346 0.2541604 0.2420575 0.2526475
### Example 2 ###
E <- c(1,2,3)
S <- length(E)
init.distr <- rep(1/S, 3)
p <- matrix(c(0.3,0.2,0.5,0.1,0.6,0.3,0.2,0.4,0.4), nrow = 3, byrow = TRUE)
## simulation with the simulMk function
seq1 = simulMk(E = E, nbSeq = 1, lengthSeq = 100, Ptrans = p, init = init.distr, k = 1)
## estimation
res1 = estimMk(seq = seq1, E = E, k = 1)
## results of estimation
# initial law
res1$init
# [1] 0.1507212 0.4062408 0.4430380
# transition matrix
res1$Ptrans
# [,1] [,2] [,3]
# [1,] 0.2500000 0.1875000 0.5625000
# [2,] 0.0500000 0.5500000 0.4000000
# [3,] 0.2093023 0.3488372 0.4418605