Package 'SMM' reference manual

Title:	Simulation and Estimation of Multi-State Discrete-Time Semi-Markov and Markov Models
Description:	Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. Estimation and simulation of discrete-time k-th order Markov chains are also considered.
Authors:	Vlad Stefan Barbu, Caroline Berard, Dominique Cellier, Mathilde Sautreuil and Nicolas Vergne
Maintainer:	Nicolas Vergne <[email protected]>
License:	GPL
Version:	1.0.2
Built:	2025-03-09 03:40:52 UTC
Source:	https://github.com/cran/SMM

SMM : Semi-Markov and Markov Models

Description

This package performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. Estimation and simulation of discrete-time k-th order Markov chains are also considered.

Details

This R package provides the following functions: estimSM, simulSM, LoglikelihoodSM, AIC_SM, BIC_SM, InitialLawSM, estimMk, simulMk, LoglikelihoodMK, AIC_Mk, BIC_Mk and InitialLawMk.

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

Examples

##--------------------------------------##
#  An example for the simulMk function   #
#  see more examples >help(simulMk)      #
##--------------------------------------##
### Example 1 ###
# Second order model with the finite 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)

# We simulate 3 sequences of size 1000, 10000 and 2000 respectively
simulMk(E = E, nbSeq = 3, lengthSeq = c(1000, 10000, 2000), Ptrans = p, 
init = init.distribution, k = k)

##--------------------------------------##
#  An example for the estimMk function   #
#  see more examples >help(estimMk)      #
##--------------------------------------##
### Example 1 ###
# Second order model with the finite 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)

##--------------------------------------##
#  An example for the simulSM function   #
#  see more examples >help(simulSM)      #
##--------------------------------------##
alphabet = c("a","c","g","t")
S = length(alphabet)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
# 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)

################################
## Parametric simulation of a trajectory (of length equal to 50) 
## where the sojourn times do not depend on the present state and on the next state
################################
## Simulation of a sequence of length 50
seq50 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 50, TypeSojournTime = "f", 
                init = vect.init, Ptrans = Pij, distr = "pois", param = 2)
                
##--------------------------------------##
#  An example for the simulSM function   #
#  see more examples >help(simulSM)      #
##--------------------------------------##
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

################################
## Parametric estimation of a trajectory (of length equal to 5000) 
## where the sojourn times do not depend on the present state and on the next state
################################
## Simulation of a sequence of length 5000
seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", 
                init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2)
                
                
## Estimation of the simulated sequence                
estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f", 
                   distr = "pois", cens.end = 0, cens.beg = 0)
                   
##--------------------------------------##
#  An example for the simulMk function   #
#  see more examples >help(simulMk)      #
##--------------------------------------##
### Example 1 ###
# Second order model with the finite 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)

# We simulate 3 sequences of size 1000, 10000 and 2000 respectively
simulMk(E = E, nbSeq = 3, lengthSeq = c(1000, 10000, 2000), Ptrans = p, 
init = init.distribution, k = k)

##--------------------------------------##
#  An example for the estimMk function   #
#  see more examples >help(estimMk)      #
##--------------------------------------##
### Example 1 ###
# Second order model with the finite 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)

##--------------------------------------##
#  An example for the simulSM function   #
#  see more examples >help(simulSM)      #
##--------------------------------------##
alphabet = c("a","c","g","t")
S = length(alphabet)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
# 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)

################################
## Parametric simulation of a trajectory (of length equal to 50) 
## where the sojourn times do not depend on the present state and on the next state
################################
## Simulation of a sequence of length 50
seq50 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 50, TypeSojournTime = "f", 
                init = vect.init, Ptrans = Pij, distr = "pois", param = 2)
                
##--------------------------------------##
#  An example for the simulSM function   #
#  see more examples >help(simulSM)      #
##--------------------------------------##
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

################################
## Parametric estimation of a trajectory (of length equal to 5000) 
## where the sojourn times do not depend on the present state and on the next state
################################
## Simulation of a sequence of length 5000
seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", 
                init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2)
                
                
## Estimation of the simulated sequence                
estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f", 
                   distr = "pois", cens.end = 0, cens.beg = 0)

AIC (Markov model)

Description

AIC

Usage

AIC_Mk(seq, E, mu, Ptrans, k)
AIC_Mk(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 Markov model

Details

$AIC(M) = -2*log{L} + 2*M$ , where L is the log-likelihood, M is the number of parameters of the model.

Value

AIC

List: value of AIC for each sequence

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

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 AIC
#################################
AIC_Mk(seq = seq2, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, k = 1)

#[[1]]
#[1] 60.20263
#
#[[2]]
#[1] 115.7674
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

AIC (semi-Markov model)

Description

AIC

Usage

AIC_SM(seq, E, mu, Ptrans, distr = "NP", param = NULL, laws = NULL, TypeSojournTime)
AIC_SM(seq, E, mu, Ptrans, distr = "NP", param = NULL, laws = NULL, TypeSojournTime)

Arguments

`seq`	List of sequence(s)
`E`	Vector of state space of length S
`mu`	Vector of initial distribution
`Ptrans`	Matrix of transition probabilities of the embedded Markov chain $J=(J_m)_{m}$
`distr`	- "NP" for nonparametric case, laws have to be used, param is useless - Matrix of distributions of size SxS if TypeSojournTime is equal to "fij"; - Vector of distributions of size S if TypeSojournTime is equal to "fi" or "fj"; - A distribution if TypeSojournTime is equal to "f". The distributions to be used in distr must be one of "uniform", "geom", "pois", "dweibull", "nbinom".
`param`	- Useless if distr = "NP" - Array of distribution parameters of size SxSx2 (2 corresponds to the maximal number of distribution parameters) if TypeSojournTime is equal to "fij"; - Matrix of distribution parameters of size Sx2 if TypeSojournTime is equal to "fi" or "fj"; - Vector of distribution parameters of length 2 if TypeSojournTime is equal to "f".
`laws`	- Useless if distr $\neq$ "NP" - Array of size SxSxKmax if TypeSojournTime is equal to "fij"; - Matrix of size SxKmax if TypeSojournTime is equal to "fi" or "fj"; - Vector of length Kmax if the TypeSojournTime is equal to "f". Kmax is the maximum length of the sojourn times.
`TypeSojournTime`	Character: "fij", "fi", "fj", "f"

Details

$AIC(M) = -2*log{L} + 2*M$ , where L is the log-likelihood, M is the number of parameters of the model.

Value

AIC

List: value of AIC for each sequence

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

Examples

alphabet = c("a","c","g","t")
S = length(alphabet)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
# 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


################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000 
## and 2000 respectively)
## with sojourn times depending on the present state and on the next state
## the sojourn times are modelled by different distributions
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the distribution matrix
distr.matrix = matrix(c("dweibull", "pois", "geom", "nbinom", "geom", "nbinom", 
"pois", "dweibull", "pois", "pois", "dweibull", "geom", "pois","geom", "geom", 
"nbinom"), nrow = S, ncol = S, byrow = TRUE)
## creation of an array containing the parameters
param1.matrix = matrix(c(0.6,2,0.4,4,0.7,2,5,0.6,2,3,0.6,0.6,4,0.3,0.4,4), 
nrow = S, ncol = S, byrow = TRUE)
param2.matrix = matrix(c(0.8,0,0,2,0,5,0,0.8,0,0,0.8,0,4,0,0,4), 
nrow = S, ncol = S, byrow = TRUE)
param.array = array(c(param1.matrix, param2.matrix), c(S,S,2))
### simulation of 3 sequences
seq3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, 
TypeSojournTime = "fij", init = vect.init, Ptrans = Pij, distr = distr.matrix,
 param = param.array, File.out = NULL)
                
#################################
## Computation of AIC
#################################
AIC_SM(seq = seq3, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, distr = distr.matrix, 
      param = param.array, TypeSojournTime = "fij")

#[[1]]
#[1] 1566.418
#
#[[2]]
#[1] 15683.48
#
#[[3]]
#[1] 3146.728
alphabet = c("a","c","g","t")
S = length(alphabet)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
# 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


################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000 
## and 2000 respectively)
## with sojourn times depending on the present state and on the next state
## the sojourn times are modelled by different distributions
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the distribution matrix
distr.matrix = matrix(c("dweibull", "pois", "geom", "nbinom", "geom", "nbinom", 
"pois", "dweibull", "pois", "pois", "dweibull", "geom", "pois","geom", "geom", 
"nbinom"), nrow = S, ncol = S, byrow = TRUE)
## creation of an array containing the parameters
param1.matrix = matrix(c(0.6,2,0.4,4,0.7,2,5,0.6,2,3,0.6,0.6,4,0.3,0.4,4), 
nrow = S, ncol = S, byrow = TRUE)
param2.matrix = matrix(c(0.8,0,0,2,0,5,0,0.8,0,0,0.8,0,4,0,0,4), 
nrow = S, ncol = S, byrow = TRUE)
param.array = array(c(param1.matrix, param2.matrix), c(S,S,2))
### simulation of 3 sequences
seq3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, 
TypeSojournTime = "fij", init = vect.init, Ptrans = Pij, distr = distr.matrix,
 param = param.array, File.out = NULL)
                
#################################
## Computation of AIC
#################################
AIC_SM(seq = seq3, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, distr = distr.matrix, 
      param = param.array, TypeSojournTime = "fij")

#[[1]]
#[1] 1566.418
#
#[[2]]
#[1] 15683.48
#
#[[3]]
#[1] 3146.728

BIC (Markov model)

Description

BIC

Usage

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

Details

$BIC(M) = -2*log{L} + log(n)*M$ , where L is the log-likelihood, M is the number of parameters of the model and n is the size of the sequence.

Value

BIC

List: value of BIC for each sequence

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

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 BIC
#################################
BIC_Mk(seq = seq2, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, k = 1)

#[[1]]
#[1] 78.39401
#
#[[2]]
#[1] 133.7015
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 BIC
#################################
BIC_Mk(seq = seq2, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, k = 1)

#[[1]]
#[1] 78.39401
#
#[[2]]
#[1] 133.7015

BIC (semi-Markov model)

Description

BIC

Usage

BIC_SM(seq, E, mu, Ptrans, distr = "NP", param = NULL, laws = NULL, TypeSojournTime)
BIC_SM(seq, E, mu, Ptrans, distr = "NP", param = NULL, laws = NULL, TypeSojournTime)

Arguments

`seq`	List of sequence(s)
`E`	Vector of state space
`mu`	Vector of initial distribution
`Ptrans`	Matrix of transition probabilities of the embedded Markov chain $J=(J_m)_{m}$
`distr`	- "NP" for nonparametric case, laws have to be used, param is useless - Matrix of distributions of size SxS if TypeSojournTime is equal to "fij"; - Vector of distributions of size S if TypeSojournTime is equal to "fi" or "fj"; - A distribution if TypeSojournTime is equal to "f". The distributions to be used in distr must be one of "uniform", "geom", "pois", "dweibull", "nbinom".
`param`	- Useless if distr = "NP" - Array of distribution parameters of size SxSx2 (2 corresponds to the maximal number of distribution parameters) if TypeSojournTime is equal to "fij"; - Matrix of distribution parameters of size Sx2 if TypeSojournTime is equal to "fi" or "fj"; - Vector of distribution parameters of length 2 if TypeSojournTime is equal to "f".
`laws`	- Useless if distr $\neq$ "NP" - Array of size SxSxKmax if TypeSojournTime is equal to "fij"; - Matrix of size SxKmax if TypeSojournTime is equal to "fi" or "fj"; - Vector of length Kmax if the TypeSojournTime is equal to "f". Kmax is the maximum length of the sojourn times.
`TypeSojournTime`	Character: "fij", "fi", "fj", "f"

Details

$BIC(M) = -2*log{L} + log(n)*M$ , where L is the log-likelihood, M is the number of parameters of the model.

Value

BIC

List: value of BIC for each sequence

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

Examples

alphabet = c("a","c","g","t")
S = length(alphabet)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
# 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


################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000
## and 2000 respectively)
## where the sojourn time depend on the present state and the next state
## the sojourn time is modelled by different distributions
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the distribution matrix
distr.matrix = matrix(c("dweibull", "pois", "geom", "nbinom", "geom", "nbinom", 
"pois", "dweibull", "pois", "pois", "dweibull", "geom", "pois","geom", "geom",
 "nbinom"), nrow = S, ncol = S, byrow = TRUE)
## creation of an array containing the parameters
param1.matrix = matrix(c(0.6,2,0.4,4,0.7,2,5,0.6,2,3,0.6,0.6,4,0.3,0.4,4),
 nrow = S, ncol = S, byrow = TRUE)
param2.matrix = matrix(c(0.8,0,0,2,0,5,0,0.8,0,0,0.8,0,4,0,0,4),
 nrow = S, ncol = S, byrow = TRUE)
param.array = array(c(param1.matrix, param2.matrix), c(S,S,2))
## Simulation of 3 sequences
seq3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3,
 TypeSojournTime = "fij", init = vect.init, Ptrans = Pij, distr = distr.matrix,
 param = param.array, File.out = NULL)
                
#################################
## Computation of BIC
#################################
BIC_SM(seq = seq3, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, distr = distr.matrix, 
      param = param.array, TypeSojournTime = "fij")

#[[1]]
#[1] 1607.322
#
#[[2]]
#[1] 15846.36
#
#[[3]]
#[1] 3121.074
alphabet = c("a","c","g","t")
S = length(alphabet)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
# 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


################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000
## and 2000 respectively)
## where the sojourn time depend on the present state and the next state
## the sojourn time is modelled by different distributions
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the distribution matrix
distr.matrix = matrix(c("dweibull", "pois", "geom", "nbinom", "geom", "nbinom", 
"pois", "dweibull", "pois", "pois", "dweibull", "geom", "pois","geom", "geom",
 "nbinom"), nrow = S, ncol = S, byrow = TRUE)
## creation of an array containing the parameters
param1.matrix = matrix(c(0.6,2,0.4,4,0.7,2,5,0.6,2,3,0.6,0.6,4,0.3,0.4,4),
 nrow = S, ncol = S, byrow = TRUE)
param2.matrix = matrix(c(0.8,0,0,2,0,5,0,0.8,0,0,0.8,0,4,0,0,4),
 nrow = S, ncol = S, byrow = TRUE)
param.array = array(c(param1.matrix, param2.matrix), c(S,S,2))
## Simulation of 3 sequences
seq3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3,
 TypeSojournTime = "fij", init = vect.init, Ptrans = Pij, distr = distr.matrix,
 param = param.array, File.out = NULL)
                
#################################
## Computation of BIC
#################################
BIC_SM(seq = seq3, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, distr = distr.matrix, 
      param = param.array, TypeSojournTime = "fij")

#[[1]]
#[1] 1607.322
#
#[[2]]
#[1] 15846.36
#
#[[3]]
#[1] 3121.074

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)
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 $X_1, X_2, ..., X_n$ be a trajectory of length $n$ of the Markov chain $X = (X_m)_{m \in N}$ of order k=1 with transition matrix $Ptrans(i,j) = P(X_{m+1} = j | X_m = i).$ The estimation of the transition matrix is $\widehat{Ptrans}(i,j) = N_{ij}/N_{i.},$ where $N_{ij}$ is the number of transitions from state i to state j and $N_{i.}$ 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 $init = P(X_1 = i).$ 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 $init = N_i/N$ , where $N_i$ is the number occurences of state i in the sequences and $N$ 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, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

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

### 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

Estimation of a semi-Markov chain

Description

Estimation of a semi-Markov chain starting from one or several sequences. This estimation can be parametric or non-parametric, non-censored, censored at the beginning and/or at the end of the sequence, with one or several trajectories. Several parametric distributions are considered (Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial).

Usage

# parametric case
estimSM(file = NULL, seq, E, TypeSojournTime = "fij", distr, cens.end = 0, 
cens.beg = 0)

estimSM(file, seq = NULL, E, TypeSojournTime = "fij", distr, cens.end = 0, 
cens.beg = 0)

# non-parametric case
estimSM(file = NULL, seq, E, TypeSojournTime = "fij", distr = "NP", cens.end = 0, 
cens.beg = 0)

estimSM(file, seq = NULL, E, TypeSojournTime = "fij", distr = "NP", cens.end = 0, 
cens.beg = 0)
# parametric case
estimSM(file = NULL, seq, E, TypeSojournTime = "fij", distr, cens.end = 0, 
cens.beg = 0)

estimSM(file, seq = NULL, E, TypeSojournTime = "fij", distr, cens.end = 0, 
cens.beg = 0)

# non-parametric case
estimSM(file = NULL, seq, E, TypeSojournTime = "fij", distr = "NP", cens.end = 0, 
cens.beg = 0)

estimSM(file, seq = NULL, E, TypeSojournTime = "fij", distr = "NP", cens.end = 0, 
cens.beg = 0)

Arguments

`file`	Path of the file in fasta format which contains the sequences from which to estimate
`seq`	List of the sequence(s)
`E`	Vector of state space of length S
`TypeSojournTime`	Character: "fij", "fi", "fj", "f" (for more explanations, see Details)
`distr`	- "NP" for nonparametric case, laws have to be used, param is useless - Matrix of distributions of size SxS if TypeSojournTime is equal to "fij"; - Vector of distributions of size S if TypeSojournTime is equal to "fi" or "fj"; - A distribution if TypeSojournTime is equal to "f". The distributions to be used in distr must be one of "uniform", "geom", "pois", "dweibull", "nbinom".
`cens.beg`	Type of censoring at the beginning of sample paths; 1 (if the first sojourn time is censored) or 0 (if not). All the sequences must be censored in the same way.
`cens.end`	Type of censoring at the end of sample paths; 1 (if the last sojourn time is censored) or 0 (if not). All the sequences must be censored in the same way.

Details

This function estimates a semi-Markov model in parametric and non-parametric case, taking into account the type of sojourn time and the censoring described in references. The non-parametric estimation concerns sojourn time distributions defined by the user. For the parametric estimation, several discrete distributions are considered (see below).

The difference between the Markov model and the semi-Markov model concerns the modelisation of the sojourn time. With a Markov chain, the sojourn time distribution is modeled by a Geometric distribution (in discrete time). With a semi-Markov chain, the sojourn time can be any arbitrary distribution. In this package, the available distribution for a semi-Markov model are :

Uniform: $f(x) = 1/n$ for $a \le x \le b$ , with $n = b-a+1$
Geometric: $f(x) = \theta (1-\theta)^x$ for $x = 0, 1, 2,\ldots,n$ , $0 < \theta \le 1$ , with $n > 0$ and $\theta$ is the probability of success
Poisson: $f(x) = (\lambda^x exp(-\lambda))/x!$ for $x = 0, 1, 2,\ldots,n$ , with $n > 0$ and $\lambda > 0$
Discrete Weibull of type 1: $f(x)=q^{(x-1)^{\beta}}-q^{x^{\beta}}, x=1,2,3,\ldots,n$ , with $n > 0$ , $q$ is the first parameter and $\beta$ is the second parameter
Negative binomial: $f(x)=\Gamma(x+\alpha)/(\Gamma(\alpha) x!) (\alpha/(\alpha+\mu))^{\alpha} (\mu/(\alpha+\mu))^x$ , for $x = 0, 1, 2,\ldots,n$ , $n > 0,\ \Gamma$ is the Gamma function, $\alpha$ is the parameter of overdispersion and $\mu$ is the mean
Non-parametric

We define :

the semi-Markov kernel $q_{ij}(k) = P( J_{m+1} = j, T_{m+1} - T_{m} = k | J_{m} = i )$ ;
the transition matrix $(Ptrans(i,j))_{i,j} \in E$ of the embedded Markov chain $J = (J_m)_m$ , $Ptrans(i,j) = P( J_{m+1} = j | J_m = i )$ ;
the initial distribution $init = P(J_1 = i) = P(Y_1 = i)$ ;
the conditional sojourn time distributions $(f_{ij}(k))_{i,j} \in E,\ k \in N ,\ f_{ij}(k) = P(T_{m+1} - T_m = k | J_m = i, J_{m+1} = j )$ , f is specified by the argument "param" in the parametric case and by "laws" in the non-parametric case.

The estimation of the transition matrix of the embedded Markov chain is $\widehat{Ptrans}(i,j) = N_{ij}/N_{i.}.$

The estimation of the initial law is the limit law if the number of sequences is equal to 1, else the estimation of the inital law is $init = N_i^l/N^l$ , where $N_i^l$ is the number of times the state i appears in all the sequences and $N^l$ is the size of sequences.

In the parametric case, the distribution of sojourn time is calculated with the estimated parameters.

Note that $q_{ij}(k) = Ptrans(i,j)*f_{ij}(k)$ in the general case (depending on the present state and on the next state). For particular cases, we replace $f_{ij}(k)$ by $f_{i.}(k)$ (depending on the present state "fi."), $f_{.j}(k)$ (depending on the next state "f.j") and $f_{..}(k)$ (depending neither on the present state nor on the next state "f").

In this package we can choose differents types of sojourn time. Four options are available for the sojourn times:

depending on the present state and on the next state ("fij");
depending only on the present state ("fi");
depending only on the next state ("fj");
depending neither on the current, nor on the next state ("f").

If TypeSojournTime is equal to "fij", distr is a matrix (SxS) (e.g., if the row 1 of the 2nd column is "pois", that is to say we go from the first state to the second state following a Poisson distribution). If TypeSojournTime is equal to "fi" or "fj", distr must be a vector (e.g., if the first element of vector is "geom", that is to say we go from the first state to any state according to a Geometric distribution). If TypeSojournTime is equal to "f", distr must be one of "uniform", "geom", "pois", "dweibull", "nbinom" (e.g., if distr is equal to "nbinom", that is to say that the sojourn times when going from any state to any state follows a Negative Binomial distribution). For the non-parametric case, distr is equal to "NP" whatever type of sojourn time given.

If the sequence is censored at the beginning and at the end, cens.beg must be equal to 1 and cens.end must be equal to 1 too. All the sequences must be censored in the same way.

At any use of the function, a file in .txt format is created. This file contains the output of the function.

Value

`init`	Vector of initial distribution
`Ptrans`	Matrix of transition probabilities of the embedded Markov chain $J=(J_m)_{m}$ of size SxS
`param`	- Useless if distr = "NP" - Array of distribution parameters of size SxSx2 (2 corresponds to the maximal number of distribution parameters) if TypeSojournTime is equal to "fij"; - Matrix of distribution parameters of size Sx2 if TypeSojournTime is equal to "fi" or "fj"; - Vector of distribution parameters of length 2 if TypeSojournTime is equal to "f".
`laws`	- Useless if distr $\neq$ "NP" - Array of size SxSxKmax if TypeSojournTime is equal to "fij"; - Matrix of size SxKmax if TypeSojournTime is equal to "fi" or "fj"; - Vector of length Kmax if the TypeSojournTime is equal to "f". Kmax is the maximum length of the sojourn times.
`q`	Array of size SxSxKmax with the estimated semi-Markov kernel

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

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

################################
## Parametric estimation of a trajectory (of length equal to 5000)
## where the sojourn times depend neither on the present state nor on the next state ("f")
################################
## Simulation of a sequence of length 5000
seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f",
               init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2)


## Estimation of the simulated sequence
estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f",
                  distr = "pois", cens.end = 0, cens.beg = 0)

# initial distribution estimated
estSeq5000$init
# [1] 0.3592058 0.1456077 0.1600481 0.3351384

# transition matrix estimated
estSeq5000$Ptrans
#          [,1]       [,2]       [,3]      [,4]
#[1,] 0.0000000 0.21775544 0.30150754 0.4807370
#[2,] 0.4297521 0.00000000 0.18181818 0.3884298
#[3,] 0.1052632 0.23308271 0.00000000 0.6616541
#[4,] 0.8348294 0.08976661 0.07540395 0.0000000

# estimated parameter
estSeq5000$param
# [1] 2.007822 0.000000

# estimated semi-Markov kernel
estSeq5000$q
# , , 1
#           [,1]       [,2]       [,3]       [,4]
#[1,] 0.00000000 0.02924038 0.04048668 0.06455377
#[2,] 0.05770747 0.00000000 0.02441470 0.05215867
#[3,] 0.01413482 0.03129854 0.00000000 0.08884747
#[4,] 0.11210159 0.01205393 0.01012531 0.00000000
#, , 2
#           [,1]       [,2]       [,3]      [,4]
#[1,] 0.00000000 0.05870948 0.08129005 0.1296125
#[2,] 0.11586631 0.00000000 0.04902036 0.1047253
#[3,] 0.02838021 0.06284189 0.00000000 0.1783899
#[4,] 0.22508003 0.02420215 0.02032981 0.0000000
#, , 3
#          [,1]       [,2]       [,3]      [,4]
#[1,] 0.0000000 0.05893909 0.08160797 0.1301194
#[2,] 0.1163195 0.00000000 0.04921208 0.1051349
#[3,] 0.0284912 0.06308767 0.00000000 0.1790876
#[4,] 0.2259603 0.02429681 0.02040932 0.0000000
#, , 4
#           [,1]       [,2]       [,3]       [,4]
#[1,] 0.00000000 0.03944640 0.05461809 0.08708551
#[2,] 0.07784959 0.00000000 0.03293636 0.07036405
#[3,] 0.01906842 0.04222293 0.00000000 0.11985865
#[4,] 0.15122935 0.01626122 0.01365943 0.00000000
#, , 5
#            [,1]        [,2]        [,3]       [,4]
#[1,] 0.000000000 0.019800336 0.027415850 0.04371305
#[2,] 0.039077026 0.000000000 0.016532588 0.03531962
#[3,] 0.009571498 0.021194032 0.000000000 0.06016370
#[4,] 0.075910402 0.008162409 0.006856423 0.00000000
#, , 6
#            [,1]        [,2]        [,3]      [,4]
#[1,] 0.000000000 0.007951110 0.011009229 0.0175536
#[2,] 0.015691942 0.000000000 0.006638898 0.0141831
#[3,] 0.003843573 0.008510768 0.000000000 0.0241596
#[4,] 0.030482913 0.003277733 0.002753295 0.0000000
#, , 7
#            [,1]        [,2]         [,3]        [,4]
#[1,] 0.000000000 0.002660735 0.0036840950 0.005874085
#[2,] 0.005251104 0.000000000 0.0022216210 0.004746190
#[3,] 0.001286202 0.002848018 0.0000000000 0.008084696
#[4,] 0.010200710 0.001096851 0.0009213545 0.000000000
#, , 8
#             [,1]         [,2]         [,3]        [,4]
#[1,] 0.0000000000 0.0007631832 0.0010567152 0.001684874
#[2,] 0.0015061831 0.0000000000 0.0006372313 0.001361358
#[3,] 0.0003689234 0.0008169018 0.0000000000 0.002318947
#[4,] 0.0029258870 0.0003146115 0.0002642737 0.000000000
#, , 9
#             [,1]         [,2]         [,3]         [,4]
#[1,] 0.000000e+00 1.915420e-04 2.652120e-04 0.0004228658
#[2,] 3.780184e-04 0.000000e+00 1.599309e-04 0.0003416705
#[3,] 9.259156e-05 2.050242e-04 0.000000e+00 0.0005820041
#[4,] 7.343325e-04 7.896048e-05 6.632681e-05 0.0000000000


################################
## Parametric estimation of a trajectory (of length equal to 5000), 
## where sojourn times do not depend neither on the present state nor on the next state
## and the sequence is censored at the beginning.
################################
## Simulation of a sequence of length 5000
#seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", 
#                init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2, 
#                cens.beg = 1, cens.end = 0)
                
                
## Estimation of the simulated sequence                
#estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f", 
#                   distr = "pois", cens.end = 0, cens.beg = 1)


################################
## Parametric estimation of a trajectory (of length equal to 5000), 
## where sojourn times do not depend neither on the present state nor on the next state
## and the sequence is censored at the beginning and at the end
################################
## Simulation of a sequence of legnth 5000
#seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", 
#                init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2, 
#                cens.beg = 1, cens.end = 1)
                
                
## Estimation of the simulated sequence                
#estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f", 
#                   distr = "pois", cens.end = 1, cens.beg = 1)
                   
################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000 
## and 2000 respectively),
## where the sojourn times depend on the present state and on the next state
## and the sojourn time distributions are modeled by different distributions.
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
## creation of the distribution matrix
distr.matrix = matrix(c("dweibull", "pois", "geom", "nbinom", "geom", "nbinom", 
"pois", "dweibull", "pois", "pois", "dweibull", "geom", "pois","geom", "geom", 
"nbinom"), nrow = S, ncol = S, byrow = TRUE)
## creation of an array containing the parameters
param1.matrix = matrix(c(0.6,2,0.4,4,0.7,2,5,0.6,2,3,0.6,0.6,4,0.3,0.4,4), nrow = S, 
ncol = S, byrow = TRUE)
param2.matrix = matrix(c(0.8,0,0,2,0,5,0,0.8,0,0,0.8,0,4,0,0,4), nrow = S, ncol = S, 
byrow = TRUE)
param.array = array(c(param1.matrix, param2.matrix), c(S,S,2))
### Simulation of 3 sequences
seq3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fij", 
init = vect.init, Ptrans = Pij, distr = distr.matrix, param = param.array, File.out = NULL)
                
## Estimation of the simulated sequence                
estSeq3 = estimSM(seq = seq3, E = alphabet, TypeSojournTime = "fij", 
                   distr = distr.matrix, cens.end = 0, cens.beg = 0)
                   
################################
## Non-Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000 
## and 2000 respectively),
## where the sojourn times depend on the present state and on the next state
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
## creation of an array containing the conditional distributions
Kmax = 4 
mat1 = matrix(c(0,0.5,0.4,0.6,0.3,0,0.5,0.4,0.7,0.2,0,0.3,0.4,0.6,0.2,0), nrow = S, 
ncol = S, byrow = TRUE)
mat2 = matrix(c(0,0.2,0.3,0.1,0.2,0,0.2,0.3,0.1,0.4,0,0.3,0.2,0.1,0.3,0), nrow = S, 
ncol = S, byrow = TRUE)
mat3 = matrix(c(0,0.1,0.3,0.1,0.3,0,0.1,0.2,0.1,0.2,0,0.3,0.3,0.3,0.4,0), nrow = S, 
ncol = S, byrow = TRUE)
mat4 = matrix(c(0,0.2,0,0.2,0.2,0,0.2,0.1,0.1,0.2,0,0.1,0.1,0,0.1,0), nrow = S, 
ncol = S, byrow = TRUE)
f <- array(c(mat1,mat2,mat3,mat4), c(S,S,Kmax))
### Simulation of 3 sequences
seq3.NP = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, 
TypeSojournTime = "fij", init = vect.init, Ptrans = Pij, distr = "NP", laws = f, 
File.out = NULL)
                
## Estimation of the simulated sequence                
estSeq3.NP = estimSM(seq = seq3.NP, E = alphabet, TypeSojournTime = "fij", 
                   distr = "NP", cens.end = 0, cens.beg = 0)

                 
# initial distribution estimated
estSeq3.NP$init
# [1] 0.1856190 0.2409524 0.2975714 0.2758571

# transition matrix estimated
estSeq3.NP$Ptrans
#            [,1]       [,2]       [,3]      [,4]
# [1,] 0.00000000 0.20560325 0.29191143 0.5024853
# [2,] 0.40614334 0.00000000 0.19795222 0.3959044
# [3,] 0.08932039 0.21941748 0.00000000 0.6912621
# [4,] 0.81206817 0.09120221 0.09672962 0.0000000

# parameter estimated
estSeq3.NP$laws
#, , 1
#          [,1]      [,2]      [,3]      [,4]
# [1,] 0.0000000 0.4769231 0.4009288 0.6016187
# [2,] 0.3053221 0.0000000 0.4540230 0.3764368
# [3,] 0.7717391 0.2389381 0.0000000 0.2794944
# [4,] 0.4140669 0.5959596 0.2000000 0.0000000
# , , 2
#           [,1]      [,2]      [,3]      [,4]
# [1,] 0.00000000 0.2175824 0.2801858 0.1052158
# [2,] 0.16526611 0.0000000 0.2356322 0.2959770
# [3,] 0.07608696 0.3716814 0.0000000 0.3089888
# [4,] 0.18774816 0.1161616 0.3142857 0.0000000
# , , 3
#           [,1]       [,2]      [,3]       [,4]
# [1,] 0.00000000 0.09450549 0.3188854 0.08363309
# [2,] 0.31092437 0.00000000 0.1034483 0.19540230
# [3,] 0.06521739 0.20353982 0.0000000 0.32022472
# [4,] 0.29892229 0.28787879 0.3666667 0.00000000
#, , 4
#           [,1]      [,2]      [,3]       [,4]
# [1,] 0.00000000 0.2109890 0.0000000 0.20953237
# [2,] 0.21848739 0.0000000 0.2068966 0.13218391
# [3,] 0.08695652 0.1858407 0.0000000 0.09129213
# [4,] 0.09926262 0.0000000 0.1190476 0.00000000

# semi-Markovian kernel estimated
estSeq3.NP$q
# , , 1
#           [,1]       [,2]       [,3]      [,4]
# [1,] 0.00000000 0.09805694 0.11703570 0.3023046
# [2,] 0.12400455 0.00000000 0.08987486 0.1490330
# [3,] 0.06893204 0.05242718 0.00000000 0.1932039
# [4,] 0.33625058 0.05435283 0.01934592 0.0000000
# , , 2
#            [,1]       [,2]       [,3]       [,4]
# [1,] 0.000000000 0.04473565 0.08178943 0.05286941
# [2,] 0.067121729 0.00000000 0.04664391 0.11717861
# [3,] 0.006796117 0.08155340 0.00000000 0.21359223
# [4,] 0.152464302 0.01059420 0.03040074 0.00000000
#, , 3
#            [,1]       [,2]       [,3]       [,4]
# [1,] 0.000000000 0.01943064 0.09308631 0.04202440
# [2,] 0.126279863 0.00000000 0.02047782 0.07736064
# [3,] 0.005825243 0.04466019 0.00000000 0.22135922
# [4,] 0.242745279 0.02625518 0.03546753 0.00000000
#, , 4
#           [,1]       [,2]       [,3]      [,4]
# [1,] 0.00000000 0.04338003 0.00000000 0.1052869
# [2,] 0.08873720 0.00000000 0.04095563 0.0523322
# [3,] 0.00776699 0.04077670 0.00000000 0.0631068
# [4,] 0.08060801 0.00000000 0.01151543 0.0000000

#---------------------------------------------#
alphabet = c("0","1")
S = length(alphabet)
# creation of the transition matrix
Pij = matrix(c(0,1,1,0), nrow = S, ncol = S, byrow = TRUE)
distr = matrix(c("nbinom", "pois", "geom", "geom"), nrow = S, ncol = S, byrow = TRUE)
param = array(c(matrix(c(2,5,0.4,0.7), nrow = S, ncol = S, byrow = TRUE), matrix(c(6,0,0,0), 
nrow = S, ncol = S, byrow = TRUE)), c(S,S,2)) 

################################
## Parametric estimation of a trajectory (of length  equal to 5000) 
## where the state space is {"0","1"}
################################
## Simulation of a sequence of length 5000
seq2 = simulSM(E = alphabet, NbSeq = 2, lengthSeq = c(5000,1000), TypeSojournTime = "fij", 
                init = c(1/2,1/2), Ptrans = Pij, distr = distr, param = param)
                
                
## Estimation of the simulated sequence                
estSeq2 = estimSM(seq = seq2, E = alphabet, TypeSojournTime = "fij", 
                   distr = distr, cens.end = 1, cens.beg = 1)


#---------------------------------------------#


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

################################
## Parametric estimation of a trajectory (of length equal to 5000)
## where the sojourn times depend neither on the present state nor on the next state ("f")
################################
## Simulation of a sequence of length 5000
seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f",
               init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2)


## Estimation of the simulated sequence
estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f",
                  distr = "pois", cens.end = 0, cens.beg = 0)

# initial distribution estimated
estSeq5000$init
# [1] 0.3592058 0.1456077 0.1600481 0.3351384

# transition matrix estimated
estSeq5000$Ptrans
#          [,1]       [,2]       [,3]      [,4]
#[1,] 0.0000000 0.21775544 0.30150754 0.4807370
#[2,] 0.4297521 0.00000000 0.18181818 0.3884298
#[3,] 0.1052632 0.23308271 0.00000000 0.6616541
#[4,] 0.8348294 0.08976661 0.07540395 0.0000000

# estimated parameter
estSeq5000$param
# [1] 2.007822 0.000000

# estimated semi-Markov kernel
estSeq5000$q
# , , 1
#           [,1]       [,2]       [,3]       [,4]
#[1,] 0.00000000 0.02924038 0.04048668 0.06455377
#[2,] 0.05770747 0.00000000 0.02441470 0.05215867
#[3,] 0.01413482 0.03129854 0.00000000 0.08884747
#[4,] 0.11210159 0.01205393 0.01012531 0.00000000
#, , 2
#           [,1]       [,2]       [,3]      [,4]
#[1,] 0.00000000 0.05870948 0.08129005 0.1296125
#[2,] 0.11586631 0.00000000 0.04902036 0.1047253
#[3,] 0.02838021 0.06284189 0.00000000 0.1783899
#[4,] 0.22508003 0.02420215 0.02032981 0.0000000
#, , 3
#          [,1]       [,2]       [,3]      [,4]
#[1,] 0.0000000 0.05893909 0.08160797 0.1301194
#[2,] 0.1163195 0.00000000 0.04921208 0.1051349
#[3,] 0.0284912 0.06308767 0.00000000 0.1790876
#[4,] 0.2259603 0.02429681 0.02040932 0.0000000
#, , 4
#           [,1]       [,2]       [,3]       [,4]
#[1,] 0.00000000 0.03944640 0.05461809 0.08708551
#[2,] 0.07784959 0.00000000 0.03293636 0.07036405
#[3,] 0.01906842 0.04222293 0.00000000 0.11985865
#[4,] 0.15122935 0.01626122 0.01365943 0.00000000
#, , 5
#            [,1]        [,2]        [,3]       [,4]
#[1,] 0.000000000 0.019800336 0.027415850 0.04371305
#[2,] 0.039077026 0.000000000 0.016532588 0.03531962
#[3,] 0.009571498 0.021194032 0.000000000 0.06016370
#[4,] 0.075910402 0.008162409 0.006856423 0.00000000
#, , 6
#            [,1]        [,2]        [,3]      [,4]
#[1,] 0.000000000 0.007951110 0.011009229 0.0175536
#[2,] 0.015691942 0.000000000 0.006638898 0.0141831
#[3,] 0.003843573 0.008510768 0.000000000 0.0241596
#[4,] 0.030482913 0.003277733 0.002753295 0.0000000
#, , 7
#            [,1]        [,2]         [,3]        [,4]
#[1,] 0.000000000 0.002660735 0.0036840950 0.005874085
#[2,] 0.005251104 0.000000000 0.0022216210 0.004746190
#[3,] 0.001286202 0.002848018 0.0000000000 0.008084696
#[4,] 0.010200710 0.001096851 0.0009213545 0.000000000
#, , 8
#             [,1]         [,2]         [,3]        [,4]
#[1,] 0.0000000000 0.0007631832 0.0010567152 0.001684874
#[2,] 0.0015061831 0.0000000000 0.0006372313 0.001361358
#[3,] 0.0003689234 0.0008169018 0.0000000000 0.002318947
#[4,] 0.0029258870 0.0003146115 0.0002642737 0.000000000
#, , 9
#             [,1]         [,2]         [,3]         [,4]
#[1,] 0.000000e+00 1.915420e-04 2.652120e-04 0.0004228658
#[2,] 3.780184e-04 0.000000e+00 1.599309e-04 0.0003416705
#[3,] 9.259156e-05 2.050242e-04 0.000000e+00 0.0005820041
#[4,] 7.343325e-04 7.896048e-05 6.632681e-05 0.0000000000


################################
## Parametric estimation of a trajectory (of length equal to 5000), 
## where sojourn times do not depend neither on the present state nor on the next state
## and the sequence is censored at the beginning.
################################
## Simulation of a sequence of length 5000
#seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", 
#                init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2, 
#                cens.beg = 1, cens.end = 0)
                
                
## Estimation of the simulated sequence                
#estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f", 
#                   distr = "pois", cens.end = 0, cens.beg = 1)


################################
## Parametric estimation of a trajectory (of length equal to 5000), 
## where sojourn times do not depend neither on the present state nor on the next state
## and the sequence is censored at the beginning and at the end
################################
## Simulation of a sequence of legnth 5000
#seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", 
#                init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2, 
#                cens.beg = 1, cens.end = 1)
                
                
## Estimation of the simulated sequence                
#estSeq5000 = estimSM(seq = seq5000, E = alphabet, TypeSojournTime = "f", 
#                   distr = "pois", cens.end = 1, cens.beg = 1)
                   
################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000 
## and 2000 respectively),
## where the sojourn times depend on the present state and on the next state
## and the sojourn time distributions are modeled by different distributions.
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
## creation of the distribution matrix
distr.matrix = matrix(c("dweibull", "pois", "geom", "nbinom", "geom", "nbinom", 
"pois", "dweibull", "pois", "pois", "dweibull", "geom", "pois","geom", "geom", 
"nbinom"), nrow = S, ncol = S, byrow = TRUE)
## creation of an array containing the parameters
param1.matrix = matrix(c(0.6,2,0.4,4,0.7,2,5,0.6,2,3,0.6,0.6,4,0.3,0.4,4), nrow = S, 
ncol = S, byrow = TRUE)
param2.matrix = matrix(c(0.8,0,0,2,0,5,0,0.8,0,0,0.8,0,4,0,0,4), nrow = S, ncol = S, 
byrow = TRUE)
param.array = array(c(param1.matrix, param2.matrix), c(S,S,2))
### Simulation of 3 sequences
seq3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fij", 
init = vect.init, Ptrans = Pij, distr = distr.matrix, param = param.array, File.out = NULL)
                
## Estimation of the simulated sequence                
estSeq3 = estimSM(seq = seq3, E = alphabet, TypeSojournTime = "fij", 
                   distr = distr.matrix, cens.end = 0, cens.beg = 0)
                   
################################
## Non-Parametric simulation of several trajectories (3 trajectories of length 1000, 10 000 
## and 2000 respectively),
## where the sojourn times depend on the present state and on the next state
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
## creation of an array containing the conditional distributions
Kmax = 4 
mat1 = matrix(c(0,0.5,0.4,0.6,0.3,0,0.5,0.4,0.7,0.2,0,0.3,0.4,0.6,0.2,0), nrow = S, 
ncol = S, byrow = TRUE)
mat2 = matrix(c(0,0.2,0.3,0.1,0.2,0,0.2,0.3,0.1,0.4,0,0.3,0.2,0.1,0.3,0), nrow = S, 
ncol = S, byrow = TRUE)
mat3 = matrix(c(0,0.1,0.3,0.1,0.3,0,0.1,0.2,0.1,0.2,0,0.3,0.3,0.3,0.4,0), nrow = S, 
ncol = S, byrow = TRUE)
mat4 = matrix(c(0,0.2,0,0.2,0.2,0,0.2,0.1,0.1,0.2,0,0.1,0.1,0,0.1,0), nrow = S, 
ncol = S, byrow = TRUE)
f <- array(c(mat1,mat2,mat3,mat4), c(S,S,Kmax))
### Simulation of 3 sequences
seq3.NP = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, 
TypeSojournTime = "fij", init = vect.init, Ptrans = Pij, distr = "NP", laws = f, 
File.out = NULL)
                
## Estimation of the simulated sequence                
estSeq3.NP = estimSM(seq = seq3.NP, E = alphabet, TypeSojournTime = "fij", 
                   distr = "NP", cens.end = 0, cens.beg = 0)

                 
# initial distribution estimated
estSeq3.NP$init
# [1] 0.1856190 0.2409524 0.2975714 0.2758571

# transition matrix estimated
estSeq3.NP$Ptrans
#            [,1]       [,2]       [,3]      [,4]
# [1,] 0.00000000 0.20560325 0.29191143 0.5024853
# [2,] 0.40614334 0.00000000 0.19795222 0.3959044
# [3,] 0.08932039 0.21941748 0.00000000 0.6912621
# [4,] 0.81206817 0.09120221 0.09672962 0.0000000

# parameter estimated
estSeq3.NP$laws
#, , 1
#          [,1]      [,2]      [,3]      [,4]
# [1,] 0.0000000 0.4769231 0.4009288 0.6016187
# [2,] 0.3053221 0.0000000 0.4540230 0.3764368
# [3,] 0.7717391 0.2389381 0.0000000 0.2794944
# [4,] 0.4140669 0.5959596 0.2000000 0.0000000
# , , 2
#           [,1]      [,2]      [,3]      [,4]
# [1,] 0.00000000 0.2175824 0.2801858 0.1052158
# [2,] 0.16526611 0.0000000 0.2356322 0.2959770
# [3,] 0.07608696 0.3716814 0.0000000 0.3089888
# [4,] 0.18774816 0.1161616 0.3142857 0.0000000
# , , 3
#           [,1]       [,2]      [,3]       [,4]
# [1,] 0.00000000 0.09450549 0.3188854 0.08363309
# [2,] 0.31092437 0.00000000 0.1034483 0.19540230
# [3,] 0.06521739 0.20353982 0.0000000 0.32022472
# [4,] 0.29892229 0.28787879 0.3666667 0.00000000
#, , 4
#           [,1]      [,2]      [,3]       [,4]
# [1,] 0.00000000 0.2109890 0.0000000 0.20953237
# [2,] 0.21848739 0.0000000 0.2068966 0.13218391
# [3,] 0.08695652 0.1858407 0.0000000 0.09129213
# [4,] 0.09926262 0.0000000 0.1190476 0.00000000

# semi-Markovian kernel estimated
estSeq3.NP$q
# , , 1
#           [,1]       [,2]       [,3]      [,4]
# [1,] 0.00000000 0.09805694 0.11703570 0.3023046
# [2,] 0.12400455 0.00000000 0.08987486 0.1490330
# [3,] 0.06893204 0.05242718 0.00000000 0.1932039
# [4,] 0.33625058 0.05435283 0.01934592 0.0000000
# , , 2
#            [,1]       [,2]       [,3]       [,4]
# [1,] 0.000000000 0.04473565 0.08178943 0.05286941
# [2,] 0.067121729 0.00000000 0.04664391 0.11717861
# [3,] 0.006796117 0.08155340 0.00000000 0.21359223
# [4,] 0.152464302 0.01059420 0.03040074 0.00000000
#, , 3
#            [,1]       [,2]       [,3]       [,4]
# [1,] 0.000000000 0.01943064 0.09308631 0.04202440
# [2,] 0.126279863 0.00000000 0.02047782 0.07736064
# [3,] 0.005825243 0.04466019 0.00000000 0.22135922
# [4,] 0.242745279 0.02625518 0.03546753 0.00000000
#, , 4
#           [,1]       [,2]       [,3]      [,4]
# [1,] 0.00000000 0.04338003 0.00000000 0.1052869
# [2,] 0.08873720 0.00000000 0.04095563 0.0523322
# [3,] 0.00776699 0.04077670 0.00000000 0.0631068
# [4,] 0.08060801 0.00000000 0.01151543 0.0000000

#---------------------------------------------#
alphabet = c("0","1")
S = length(alphabet)
# creation of the transition matrix
Pij = matrix(c(0,1,1,0), nrow = S, ncol = S, byrow = TRUE)
distr = matrix(c("nbinom", "pois", "geom", "geom"), nrow = S, ncol = S, byrow = TRUE)
param = array(c(matrix(c(2,5,0.4,0.7), nrow = S, ncol = S, byrow = TRUE), matrix(c(6,0,0,0), 
nrow = S, ncol = S, byrow = TRUE)), c(S,S,2)) 

################################
## Parametric estimation of a trajectory (of length  equal to 5000) 
## where the state space is {"0","1"}
################################
## Simulation of a sequence of length 5000
seq2 = simulSM(E = alphabet, NbSeq = 2, lengthSeq = c(5000,1000), TypeSojournTime = "fij", 
                init = c(1/2,1/2), Ptrans = Pij, distr = distr, param = param)
                
                
## Estimation of the simulated sequence                
estSeq2 = estimSM(seq = seq2, E = alphabet, TypeSojournTime = "fij", 
                   distr = distr, cens.end = 1, cens.beg = 1)


#---------------------------------------------#

Estimation of the initial law (Markov model)

Description

For order 1, estimation of initial law by computing the stationary law of the Markov chain.

For order greater than 1, estimation of initial law by computing the state frequencies.

Usage

InitialLawMk(E, seq, Ptrans, k)
InitialLawMk(E, seq, Ptrans, k)

Arguments

`E`	Vector of state space
`seq`	List of sequence(s)
`Ptrans`	Matrix of transition probabilities of size (S^(k))xS, with S = length(E)
`k`	Order of the Markov chain

Value

init

Vector of the initial law

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

Examples

seq = list(c("a","c","c","g","t","a","a","a","a","g","c","t","t","t","g"))
res = estimMk(seq = seq, E = c("a","c","g","t"), k = 1)
p = res$Ptrans

InitialLawMk(E = c("a","c","g","t"), seq = seq, Ptrans = p, k = 1)
seq = list(c("a","c","c","g","t","a","a","a","a","g","c","t","t","t","g"))
res = estimMk(seq = seq, E = c("a","c","g","t"), k = 1)
p = res$Ptrans

InitialLawMk(E = c("a","c","g","t"), seq = seq, Ptrans = p, k = 1)

Estimation of the initial law (semi-Markov model)

Description

For one sequence, estimation of the initial law by computating the limit law of the semi-Markovian chain.

For several sequences, estimation of the initial law by computating the first state frequencies.

Usage

InitialLawSM(E, seq, q)
InitialLawSM(E, seq, q)

Arguments

`E`	Vector of state space
`seq`	List of sequence(s)
`q`	Array of size SxSxKmax with the semi-Markov kernel (S = length(E))

Value

init

Vector of the initial distribution

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

Examples

seq = list(c("a","c","c","g","t","a","a","a","a","g","c","t","t","t","g"))
res = estimSM(seq = seq, E = c("a","c","g","t"), distr = "NP")
q = res$q
p = res$Ptrans

InitialLawSM(E = c("a","c","g","t"), seq = seq, q = q)
seq = list(c("a","c","c","g","t","a","a","a","a","g","c","t","t","t","g"))
res = estimSM(seq = seq, E = c("a","c","g","t"), distr = "NP")
q = res$q
p = res$Ptrans

InitialLawSM(E = c("a","c","g","t"), seq = seq, q = q)

Loglikelihood (Markov model)

Description

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

Usage

LoglikelihoodMk(seq, E, mu, Ptrans, k)
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, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

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

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

Loglikelihood (semi-Markov model)

Description

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

Usage

## parametric case
LoglikelihoodSM(seq, E, mu, Ptrans, distr, param, laws = NULL, TypeSojournTime)
## non-parametric case
LoglikelihoodSM(seq, E, mu, Ptrans, distr, param = NULL, laws, TypeSojournTime)
## parametric case
LoglikelihoodSM(seq, E, mu, Ptrans, distr, param, laws = NULL, TypeSojournTime)
## non-parametric case
LoglikelihoodSM(seq, E, mu, Ptrans, distr, param = NULL, laws, TypeSojournTime)

Arguments

`seq`	List of sequence(s)
`E`	Vector of state space
`mu`	Vector of initial distribution of length S
`Ptrans`	Matrix of transition probabilities of the embedded Markov chain $J=(J_m)_{m}$ of size SxS
`distr`	- "NP" for nonparametric case, laws have to be used, param is useless - Matrix of distributions of size SxS if TypeSojournTime is equal to "fij"; - Vector of distributions of size S if TypeSojournTime is equal to "fi" or "fj"; - A distribution if TypeSojournTime is equal to "f". The distributions to be used in distr must be one of "uniform", "geom", "pois", "dweibull", "nbinom".
`param`	- Useless if distr = "NP" - Array of distribution parameters of size SxSx2 (2 corresponds to the maximal number of distribution parameters) if TypeSojournTime is equal to "fij"; - Matrix of distribution parameters of size Sx2 if TypeSojournTime is equal to "fi" or "fj"; - Vector of distribution parameters of length 2 if TypeSojournTime is equal to "f".
`laws`	- Useless if distr $\neq$ "NP" - Array of size SxSxKmax if TypeSojournTime is equal to "fij"; - Matrix of size SxKmax if TypeSojournTime is equal to "fi" or "fj"; - Vector of length Kmax if the TypeSojournTime is equal to "f". Kmax is the maximum length of the sojourn times.
`TypeSojournTime`	Character: "fij", "fi", "fj", "f" (for more explanations, see Details)

Details

In this package we can choose differents types of sojourn time. Four options are available for the sojourn times:

depending on the present state and on the next state ("fij");
depending only on the present state ("fi");
depending only on the next state ("fj");
depending neither on the current, nor on the next state ("f").

Value

`L`	Value of loglikelihood for each sequence
`Kmax`	The maximal observed sojourn time

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

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

################################
## Parametric estimation of a trajectory (of length equal to 5000), 
## where the sojourn times depend neither on the present state nor on the next state.
################################
## Simulation of a sequence of length 5000
seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", 
                init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2)
                
#################################
## Computation of the loglikelihood
#################################
LoglikelihoodSM(seq = seq5000, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, 
distr = "pois", param = 2, TypeSojournTime = "f")

#$L
#$L[[1]]
#[1] -1475.348
#
#
#$Kmax
#[1] 10

#------------------------------#
################################
## Non-parametric simulation of several trajectories (3 trajectories of length 1000, 
## 10 000 and 2000 respectively),
## where the sojourn times depend on the present state and on the next state.
################################
## creation of a matrix corresponding to the conditional sojourn time distributions
lengthSeq3 = c(1000, 10000, 2000)
Kmax = 4 
mat1 = matrix(c(0,0.5,0.4,0.6,0.3,0,0.5,0.4,0.7,0.2,0,0.3,0.4,0.6,0.2,0), 
nrow = S, ncol = S, byrow = TRUE)
mat2 = matrix(c(0,0.2,0.3,0.1,0.2,0,0.2,0.3,0.1,0.4,0,0.3,0.2,0.1,0.3,0), 
nrow = S, ncol = S, byrow = TRUE)
mat3 = matrix(c(0,0.1,0.3,0.1,0.3,0,0.1,0.2,0.1,0.2,0,0.3,0.3,0.3,0.4,0), 
nrow = S, ncol = S, byrow = TRUE)
mat4 = matrix(c(0,0.2,0,0.2,0.2,0,0.2,0.1,0.1,0.2,0,0.1,0.1,0,0.1,0), 
nrow = S, ncol = S, byrow = TRUE)
f <- array(c(mat1,mat2,mat3,mat4), c(S,S,Kmax))
### Simulation of 3 sequences
seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, 
TypeSojournTime = "fij", init = rep(1/4,4), Ptrans = Pij, laws = f, 
File.out = NULL)

#################################
## Computation of the loglikelihood
#################################
LoglikelihoodSM(seq = seqNP3, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, laws = f, 
TypeSojournTime = "fij")

#$L
#$L[[1]]
#[1] -429.35
#
#$L[[2]]
#[1] -4214.521
#
#$L[[3]]
#[1] -818.6451
#
#
#$Kmax
#[1] 4
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

################################
## Parametric estimation of a trajectory (of length equal to 5000), 
## where the sojourn times depend neither on the present state nor on the next state.
################################
## Simulation of a sequence of length 5000
seq5000 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 5000, TypeSojournTime = "f", 
                init = c(1/4,1/4,1/4,1/4), Ptrans = Pij, distr = "pois", param = 2)
                
#################################
## Computation of the loglikelihood
#################################
LoglikelihoodSM(seq = seq5000, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, 
distr = "pois", param = 2, TypeSojournTime = "f")

#$L
#$L[[1]]
#[1] -1475.348
#
#
#$Kmax
#[1] 10

#------------------------------#
################################
## Non-parametric simulation of several trajectories (3 trajectories of length 1000, 
## 10 000 and 2000 respectively),
## where the sojourn times depend on the present state and on the next state.
################################
## creation of a matrix corresponding to the conditional sojourn time distributions
lengthSeq3 = c(1000, 10000, 2000)
Kmax = 4 
mat1 = matrix(c(0,0.5,0.4,0.6,0.3,0,0.5,0.4,0.7,0.2,0,0.3,0.4,0.6,0.2,0), 
nrow = S, ncol = S, byrow = TRUE)
mat2 = matrix(c(0,0.2,0.3,0.1,0.2,0,0.2,0.3,0.1,0.4,0,0.3,0.2,0.1,0.3,0), 
nrow = S, ncol = S, byrow = TRUE)
mat3 = matrix(c(0,0.1,0.3,0.1,0.3,0,0.1,0.2,0.1,0.2,0,0.3,0.3,0.3,0.4,0), 
nrow = S, ncol = S, byrow = TRUE)
mat4 = matrix(c(0,0.2,0,0.2,0.2,0,0.2,0.1,0.1,0.2,0,0.1,0.1,0,0.1,0), 
nrow = S, ncol = S, byrow = TRUE)
f <- array(c(mat1,mat2,mat3,mat4), c(S,S,Kmax))
### Simulation of 3 sequences
seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, 
TypeSojournTime = "fij", init = rep(1/4,4), Ptrans = Pij, laws = f, 
File.out = NULL)

#################################
## Computation of the loglikelihood
#################################
LoglikelihoodSM(seq = seqNP3, E = alphabet, mu = rep(1/4,4), Ptrans = Pij, laws = f, 
TypeSojournTime = "fij")

#$L
#$L[[1]]
#[1] -429.35
#
#$L[[2]]
#[1] -4214.521
#
#$L[[3]]
#[1] -818.6451
#
#
#$Kmax
#[1] 4

Simulation of a k-th order Markov chain

Description

Simulation of a k-th order Markov chain starting from a transition matrix and an initial distribution.

Usage

simulMk(E, nbSeq, lengthSeq, Ptrans, init, k, File.out = NULL)
simulMk(E, nbSeq, lengthSeq, Ptrans, init, k, File.out = NULL)

Arguments

`E`	Vector of state space
`nbSeq`	Number of simulated sequences
`lengthSeq`	Vector of size nbSeq containing the lengths of each simulated sequence
`Ptrans`	Matrix of transition probabilities of size (S^(k))xS, with S = length(E)
`init`	Vector of initial distribution of length S
`k`	Order of the Markov chain
`File.out`	Name of the fasta file for saving the sequences. If File.out = NULL, no file is created

Details

The sizes of init and Ptrans depend on S, the length of E. The rows of the transition matrix sums to 1.

For k=1, the transition matrix is defined by $Ptrans(i,j) = P(X_{m+1} = j | X_m = i)$ and the initial distribution is $init = P(X_1 = i)$ . For k > 1 we have similar expressions.

The first element of lengthSeq corresponds to the length of the first sequence and so on.

Value

simulMk returns a list of sequences of size lengthSeq simulated with a k-th order Markov chain of parameters init and Ptrans with state space E.

If the parameter File.out is not equal to NULL, a file in fasta format containing the sequence(s) will be created.

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

Examples


### Example 1 ###
# Second order model with 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)

# We simulate 3 sequences of size 1000, 10000 and 2000 respectively.
simulMk(E = E, nbSeq = 3, lengthSeq = c(1000, 10000, 2000), Ptrans = p, 
init = init.distribution, k = k)

### Example 2 ###
# first order model with state space {1,2,3}
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)

# We simulate one sequence of size 100
simulMk(E = E, nbSeq = 1, lengthSeq = 100, Ptrans = p, init = init.distr, k = 1)
### Example 1 ###
# Second order model with 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)

# We simulate 3 sequences of size 1000, 10000 and 2000 respectively.
simulMk(E = E, nbSeq = 3, lengthSeq = c(1000, 10000, 2000), Ptrans = p, 
init = init.distribution, k = k)

### Example 2 ###
# first order model with state space {1,2,3}
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)

# We simulate one sequence of size 100
simulMk(E = E, nbSeq = 1, lengthSeq = 100, Ptrans = p, init = init.distr, k = 1)

Simulation of a semi-Markov chain

Description

Simulation of a semi-Markov chain starting from chosen parameters. This simulation can be parametric or non-parametric, non-censored, censored at the beginning and/or at the end of the sequence, with one or several trajectories. Several parametric distributions are considered (Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial).

Usage

# parametric case  
simulSM(E, NbSeq, lengthSeq, TypeSojournTime = "fij", init, Ptrans, distr, param, 
laws = NULL, cens.beg = 0, cens.end = 0, File.out = NULL)

# non-parametric case
simulSM(E, NbSeq, lengthSeq, TypeSojournTime = "fij", init, Ptrans, distr = "NP", 
param = NULL, laws, cens.beg = 0, cens.end = 0, File.out = NULL)
# parametric case  
simulSM(E, NbSeq, lengthSeq, TypeSojournTime = "fij", init, Ptrans, distr, param, 
laws = NULL, cens.beg = 0, cens.end = 0, File.out = NULL)

# non-parametric case
simulSM(E, NbSeq, lengthSeq, TypeSojournTime = "fij", init, Ptrans, distr = "NP", 
param = NULL, laws, cens.beg = 0, cens.end = 0, File.out = NULL)

Arguments

`E`	Vector of state space
`NbSeq`	Number of simulated sequences
`lengthSeq`	Vector containing the lengths of each simulated sequence (the length of sequences can be different, see details)
`TypeSojournTime`	Character : must be one of "fij", "fi", "fj", "f" (for more explanations, see Details)
`init`	Vector of initial distribution of length S, with S = length(E)
`Ptrans`	Matrix of transition probabilities of the embedded Markov chain $J=(J_m)_{m}$ (The sum of the probabilities on the same row must be equal to 1. According to semi-Markov model, the probability to stay in the same state is assumed to be equal to 0.)
`distr`	- "NP" for nonparametric case, laws have to be used, param is useless - Matrix of distributions of size SxS if TypeSojournTime is equal to "fij"; - Vector of distributions of size S if TypeSojournTime is equal to "fi" or "fj"; - A distribution if TypeSojournTime is equal to "f". The distributions to be used in distr must be one of "uniform", "geom", "pois", "dweibull", "nbinom".
`param`	- Useless if distr = "NP" - Array of distribution parameters of size SxSx2 (2 corresponds to the maximal number of distribution parameters) if TypeSojournTime is equal to "fij"; - Matrix of distribution parameters of size Sx2 if TypeSojournTime is equal to "fi" or "fj"; - Vector of distribution parameters of length 2 if TypeSojournTime is equal to "f".
`laws`	- Useless if distr $\neq$ "NP" - Array of size SxSxKmax if TypeSojournTime is equal to "fij"; - Matrix of size SxKmax if TypeSojournTime is equal to "fi" or "fj"; - Vector of length Kmax if the TypeSojournTime is equal to "f". Kmax is the maximum length of the sojourn times.
`cens.beg`	Type of censoring at the beginning of sample paths; 1 (if the first sojourn time is censored) or 0 (if not). All the sequences must be censored in the same way.
`cens.end`	Type of censoring at the end of sample paths; 1 (if the last sojourn time is censored) or 0 (if not). All the sequences must be censored in the same way.
`File.out`	Name of fasta file to save the sequences; if File.out = NULL, no file is created.

Details

This function simulates a semi-Markov model in the parametric and non-parametric case, taking into account the type of sojourn time and the censoring described in references.

The non-parametric simulation concerns sojourn time distributions defined by the user. For the parametric simulation, several discrete distributions are considered (see below).

The difference between the Markov model and the semi-Markov model concerns the modelling of the sojourn time. With a Markov chain, the sojourn time distribution is modeled by a Geometric distribution. With a semi-Markov chain, the sojourn time can be arbitrarily distributed. In this package, the available distribution for a semi-Markov model are:

Uniform: $f(x) = 1/n$ for $a \le x \le b$ , with $n = b-a+1$
Geometric: $f(x) = \theta (1-\theta)^x$ for $x = 0, 1, 2,\ldots,n$ , $0 < \theta \le 1$ , with $n > 0$ and $\theta$ is the probability of success
Poisson: $f(x) = (\lambda^x exp(-\lambda))/x!$ for $x = 0, 1, 2,\ldots,n$ , with $n > 0$ and $\lambda > 0$
Discrete Weibull of type 1: $f(x)=q^{(x-1)^{\beta}}-q^{x^{\beta}}, x=1,2,3,\ldots,n$ , with $n > 0$ , $q$ is the first parameter and $\beta$ is the second parameter
Negative Binomial: $f(x)=\Gamma(x+\alpha)/(\Gamma(\alpha) x!) (\alpha/(\alpha+\mu))^{\alpha} (\mu/(\alpha+\mu))^x$ , for $x = 0, 1, 2,\ldots,n$ , $n > 0,\ \Gamma$ is the gamma function, $\alpha$ is the parameter of overdispersion and $\mu$ is the mean
Non-parametric

We define :

the semi-Markov kernel $q_{ij}(k) = P( J_{m+1} = j, T_{m+1} - T_{m} = k | J_{m} = i )$ ;
the transition matrix $(Ptrans(i,j))_{i,j} \in E$ of the embedded Markov chain $J = (J_m)_m$ , $Ptrans(i,j) = P( J_{m+1} = j | J_m = i )$ ;
the initial distribution, $init = P(J_1 = i) = P(Y_1 = i)$ ;
the conditional sojourn time distributions $(f_{ij}(k))_{i,j} \in E,\ k \in N ,\ f_{ij}(k) = P(T_{m+1} - T_m = k | J_m = i, J_{m+1} = j )$ , f is specified by the argument "param" in the parametric case and by "laws" in the non-parametric case.

In this package we can choose differents types of sojourn times. Four options are available for the sojourn times

depending on the present state and on the next state ("fij");
depending only on the present state ("fi");
depending only on the next state ("fj");
depending neither on the current, nor on the next state ("f").

If the sequence is censored at the beginning and at the end, cens.beg must be equal to 1 and cens.end must be equal to 1 too. All the sequences must be censored in the same way.

Moreover, in the non-parametric case TypeSojournTime is equal to "fij" then laws must be an array of size SxSxKmax. If distr is equal to "NP" and TypeSojournTime is equal to "fi" or "fj" then laws must be a matrix of size SxKmax. If the distr is equal to "NP" and TypeSojournTime is equal to "f" then laws is a vector of length Kmax.

For the simulation of a non-censored sequence, the length of the sequence can be greater than lengthSeq.

Value

simulSM returns sequences of size lengthSeq or greater

If File.out is not NULL, a fasta file will be created containing the sequences.

Author(s)

Vlad Stefan Barbu, [email protected]
Caroline Berard, [email protected]
Dominique Cellier, [email protected]
Mathilde Sautreuil, [email protected]
Nicolas Vergne, [email protected]

References

VS Barbu, C Berard, D Cellier, M Sautreuil and N Vergne (2017), Parametric estimation of semi-Markov chains, submitted

Examples

alphabet = c("a","c","g","t")
S = length(alphabet)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
# 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)

################################
## Parametric simulation of a trajectory (of length equal to 50), 
## where the sojourn times depend neither on the present state nor on the next state ("f").
################################
## Simulation of a sequence of length 50
seq50 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 50, TypeSojournTime = "f", 
                init = vect.init, Ptrans = Pij, distr = "pois", param = 2)
          
      
################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 
## 10 000 and  2000 respectively),
## where the sojourn times depend on the present state and on the next state ("fij")
## the sojourn time is modelled by different distributions
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the distribution matrix
distr.matrix = matrix(c("dweibull", "pois", "geom", "nbinom", "geom", "nbinom", 
"pois", "dweibull", "pois", "pois", "dweibull", "geom", "pois","geom", "geom", 
"nbinom"), nrow = S, ncol = S, byrow = TRUE)
## creation of an array containing the parameters
param1.matrix = matrix(c(0.6,2,0.4,4,0.7,2,5,0.6,2,3,0.6,0.6,4,0.3,0.4,4), 
nrow = S, ncol = S, byrow = TRUE)
param2.matrix = matrix(c(0.8,0,0,2,0,5,0,0.8,0,0,0.8,0,4,0,0,4), nrow = S, 
ncol = S, byrow = TRUE)
param.array = array(c(param1.matrix, param2.matrix), c(S,S,2))
### Simulation of 3 sequences
seq3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, 
TypeSojournTime = "fij", init = vect.init, Ptrans = Pij, distr = distr.matrix, 
param = param.array, File.out = NULL)
                

################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 
## 10 000 and  2000 respectively),
## where the sojourn times depend only on the present state ("fi.")
## and the sojourn times are modelled by different distributions.
################################
## creation of the distribution matrix
distr.vect = c("dweibull", "pois", "geom", "nbinom")
## creation of an array containing the parameters
param.matrix = matrix(c(0.6,0.8,4,0,0.7,0,5,2), nrow = S, ncol = 2, byrow = TRUE)
### Simulation of 3 sequences without censoring
#seqFi3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fi", 
#             init = vect.init, Ptrans = Pij, distr = distr.vect, param = param.matrix,
#             File.out = NULL)
### Simulation of 3 sequences with censoring at the beginning
#seqFi3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fi", 
#             init = vect.init, Ptrans = Pij, distr = distr.vect, param = param.matrix, 
#             File.out = NULL, cens.beg = 1, cens.end = 0)
### Simulation of 3 sequences with censoring at the end
#seqFi3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fi", 
#             init = vect.init, Ptrans = Pij, distr = distr.vect, param = param.matrix, 
#             File.out = NULL, cens.beg = 0, cens.end = 1)
### Simulation of 3 sequences with censoring at the beginning and at the end
#seqFi3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fi", 
#             init = vect.init, Ptrans = Pij, distr = distr.vect, param = param.matrix,
#             File.out = NULL, cens.beg = 1, cens.end = 1)
                
################################
## Non-parametric simulation of several trajectories (3 trajectories of length 1000,  
## 10 000 and  2000 respectively),
## where the sojourn times depend on the present state and on the next state ("fij")
################################
## creation of a matrix corresponding to the conditionnal sojourn time distribution
Kmax = 4 
mat1 = matrix(c(0,0.5,0.4,0.6,0.3,0,0.5,0.4,0.7,0.2,0,0.3,0.4,0.6,0.2,0), nrow = S, 
ncol = S, byrow = TRUE)
mat2 = matrix(c(0,0.2,0.3,0.1,0.2,0,0.2,0.3,0.1,0.4,0,0.3,0.2,0.1,0.3,0), nrow = S, 
ncol = S, byrow = TRUE)
mat3 = matrix(c(0,0.1,0.3,0.1,0.3,0,0.1,0.2,0.1,0.2,0,0.3,0.3,0.3,0.4,0), nrow = S, 
ncol = S, byrow = TRUE)
mat4 = matrix(c(0,0.2,0,0.2,0.2,0,0.2,0.1,0.1,0.2,0,0.1,0.1,0,0.1,0), nrow = S, 
ncol = S, byrow = TRUE)
f <- array(c(mat1,mat2,mat3,mat4), c(S,S,Kmax))
### Simulation of 3 sequences
seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fij", 
                init = vect.init, Ptrans = Pij, laws = f, File.out = NULL)


################################
## Non-parametric simulation of several trajectories (3 trajectories of length 1000, 
## 10 000 and  2000 respectively),
## where the sojourn times depend only on he next state ("fj")
################################
## creation of a matrix corresponding to the conditional sojourn time distributions
Kmax = 6
nparam.matrix = matrix(c(0.2,0.1,0.3,0.2,0.2,0,0.4,0.2,0.1,0,0.2,0.1,
 0.5,0.3,0.15,0.05,0,0,0.3,0.2,0.1,0.2,0.2,0), 
                       nrow = S, ncol = Kmax, byrow = TRUE)
### Simulation of 3 sequences without censoring
#seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fj", 
#                init = vect.init, Ptrans = Pij, laws = nparam.matrix, File.out = NULL)
### Simulation of 3 sequences with censoring at the beginnig 
#seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fj", 
#                init = vect.init, Ptrans = Pij, laws = nparam.matrix, File.out = NULL, 
#                cens.beg = 1, cens.end = 0)
### Simulation of 3 sequences with censoring at the end 
#seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fj", 
#                init = vect.init, Ptrans = Pij, laws = nparam.matrix, File.out = NULL, 
#                cens.beg = 0, cens.end = 1)
### Simulation of 3 sequences with censoring at the beginning and at the end 
#seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fj", 
#                init = vect.init, Ptrans = Pij, laws = nparam.matrix, File.out = NULL, 
#                cens.beg = 1, cens.end = 1)

alphabet = c("a","c","g","t")
S = length(alphabet)
## creation of the initial distribution
vect.init = c(1/4,1/4,1/4,1/4)
# 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)

################################
## Parametric simulation of a trajectory (of length equal to 50), 
## where the sojourn times depend neither on the present state nor on the next state ("f").
################################
## Simulation of a sequence of length 50
seq50 = simulSM(E = alphabet, NbSeq = 1, lengthSeq = 50, TypeSojournTime = "f", 
                init = vect.init, Ptrans = Pij, distr = "pois", param = 2)
          
      
################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 
## 10 000 and  2000 respectively),
## where the sojourn times depend on the present state and on the next state ("fij")
## the sojourn time is modelled by different distributions
################################
lengthSeq3 = c(1000, 10000, 2000)
## creation of the distribution matrix
distr.matrix = matrix(c("dweibull", "pois", "geom", "nbinom", "geom", "nbinom", 
"pois", "dweibull", "pois", "pois", "dweibull", "geom", "pois","geom", "geom", 
"nbinom"), nrow = S, ncol = S, byrow = TRUE)
## creation of an array containing the parameters
param1.matrix = matrix(c(0.6,2,0.4,4,0.7,2,5,0.6,2,3,0.6,0.6,4,0.3,0.4,4), 
nrow = S, ncol = S, byrow = TRUE)
param2.matrix = matrix(c(0.8,0,0,2,0,5,0,0.8,0,0,0.8,0,4,0,0,4), nrow = S, 
ncol = S, byrow = TRUE)
param.array = array(c(param1.matrix, param2.matrix), c(S,S,2))
### Simulation of 3 sequences
seq3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, 
TypeSojournTime = "fij", init = vect.init, Ptrans = Pij, distr = distr.matrix, 
param = param.array, File.out = NULL)
                

################################
## Parametric simulation of several trajectories (3 trajectories of length 1000, 
## 10 000 and  2000 respectively),
## where the sojourn times depend only on the present state ("fi.")
## and the sojourn times are modelled by different distributions.
################################
## creation of the distribution matrix
distr.vect = c("dweibull", "pois", "geom", "nbinom")
## creation of an array containing the parameters
param.matrix = matrix(c(0.6,0.8,4,0,0.7,0,5,2), nrow = S, ncol = 2, byrow = TRUE)
### Simulation of 3 sequences without censoring
#seqFi3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fi", 
#             init = vect.init, Ptrans = Pij, distr = distr.vect, param = param.matrix,
#             File.out = NULL)
### Simulation of 3 sequences with censoring at the beginning
#seqFi3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fi", 
#             init = vect.init, Ptrans = Pij, distr = distr.vect, param = param.matrix, 
#             File.out = NULL, cens.beg = 1, cens.end = 0)
### Simulation of 3 sequences with censoring at the end
#seqFi3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fi", 
#             init = vect.init, Ptrans = Pij, distr = distr.vect, param = param.matrix, 
#             File.out = NULL, cens.beg = 0, cens.end = 1)
### Simulation of 3 sequences with censoring at the beginning and at the end
#seqFi3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fi", 
#             init = vect.init, Ptrans = Pij, distr = distr.vect, param = param.matrix,
#             File.out = NULL, cens.beg = 1, cens.end = 1)
                
################################
## Non-parametric simulation of several trajectories (3 trajectories of length 1000,  
## 10 000 and  2000 respectively),
## where the sojourn times depend on the present state and on the next state ("fij")
################################
## creation of a matrix corresponding to the conditionnal sojourn time distribution
Kmax = 4 
mat1 = matrix(c(0,0.5,0.4,0.6,0.3,0,0.5,0.4,0.7,0.2,0,0.3,0.4,0.6,0.2,0), nrow = S, 
ncol = S, byrow = TRUE)
mat2 = matrix(c(0,0.2,0.3,0.1,0.2,0,0.2,0.3,0.1,0.4,0,0.3,0.2,0.1,0.3,0), nrow = S, 
ncol = S, byrow = TRUE)
mat3 = matrix(c(0,0.1,0.3,0.1,0.3,0,0.1,0.2,0.1,0.2,0,0.3,0.3,0.3,0.4,0), nrow = S, 
ncol = S, byrow = TRUE)
mat4 = matrix(c(0,0.2,0,0.2,0.2,0,0.2,0.1,0.1,0.2,0,0.1,0.1,0,0.1,0), nrow = S, 
ncol = S, byrow = TRUE)
f <- array(c(mat1,mat2,mat3,mat4), c(S,S,Kmax))
### Simulation of 3 sequences
seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fij", 
                init = vect.init, Ptrans = Pij, laws = f, File.out = NULL)


################################
## Non-parametric simulation of several trajectories (3 trajectories of length 1000, 
## 10 000 and  2000 respectively),
## where the sojourn times depend only on he next state ("fj")
################################
## creation of a matrix corresponding to the conditional sojourn time distributions
Kmax = 6
nparam.matrix = matrix(c(0.2,0.1,0.3,0.2,0.2,0,0.4,0.2,0.1,0,0.2,0.1,
 0.5,0.3,0.15,0.05,0,0,0.3,0.2,0.1,0.2,0.2,0), 
                       nrow = S, ncol = Kmax, byrow = TRUE)
### Simulation of 3 sequences without censoring
#seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fj", 
#                init = vect.init, Ptrans = Pij, laws = nparam.matrix, File.out = NULL)
### Simulation of 3 sequences with censoring at the beginnig 
#seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fj", 
#                init = vect.init, Ptrans = Pij, laws = nparam.matrix, File.out = NULL, 
#                cens.beg = 1, cens.end = 0)
### Simulation of 3 sequences with censoring at the end 
#seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fj", 
#                init = vect.init, Ptrans = Pij, laws = nparam.matrix, File.out = NULL, 
#                cens.beg = 0, cens.end = 1)
### Simulation of 3 sequences with censoring at the beginning and at the end 
#seqNP3 = simulSM(E = alphabet, NbSeq = 3, lengthSeq = lengthSeq3, TypeSojournTime = "fj", 
#                init = vect.init, Ptrans = Pij, laws = nparam.matrix, File.out = NULL, 
#                cens.beg = 1, cens.end = 1)

Package 'SMM'

Help Index

SMM : Semi-Markov and Markov Models

Description

Details

Author(s)

See Also

Examples

AIC (Markov model)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

AIC (semi-Markov model)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

BIC (Markov model)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

BIC (semi-Markov model)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Estimation of a k-th order Markov chain

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Estimation of a semi-Markov chain

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Estimation of the initial law (Markov model)

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Estimation of the initial law (semi-Markov model)

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

Loglikelihood (Markov model)

Description