smmR (version 1.0.5)

plot.smmfit: Plot function for an object of class smmfit

Description

Displays the densities for the conditional sojourn time distributions depending on the current state i and on the next state j.

Usage

# S3 method for smmfit
plot(x, i, j, klim = NULL, ...)

Value

None.

Arguments

x

An object of class smmfit (inheriting from the S3 classes smm, smmnonparametric or smmparametric).

i

An element of the state space vector x$states giving the current state in the following cases: type.sojourn = "fij" or type.sojourn = "fi", otherwise, i is ignored.

j

An element of the state space vector x$states giving the next state in the following cases: type.sojourn = "fij" or type.sojourn = "fj", otherwise, j is ignored.

klim

An integer giving the limit value for which the density will be plotted. If klim is NULL, then quantile or order 0.95 is used.

...

Arguments passed to plot.

References

V. S. Barbu, N. Limnios. (2008). Semi-Markov Chains and Hidden Semi-Markov Models Toward Applications - Their Use in Reliability and DNA Analysis. New York: Lecture Notes in Statistics, vol. 191, Springer.

Examples

states <- c("a", "c", "g", "t")
s <- length(states)

# 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, 0.4, 0.2, 0, 0.5, 0.2, 0.5, 
                0.3, 0, 0.4, 0.3, 0.5, 0.2, 0), ncol = s)

# Creation of the distribution matrix
distr.vec <- c("pois", "geom", "geom", "geom")
parameters <- matrix(c(2, 0.6, 0.8, 0.8, NA, NA, NA, NA), 
                     ncol = 2, byrow = FALSE)

# Specify the semi-Markov model
smm <- smmparametric(states = states, init = vect.init, ptrans = pij, 
                     type.sojourn = "fi", distr = distr.vec, param = parameters)

seqs <- simulate(object = smm, nsim = rep(5000, 100), seed = 10)

est <- fitsmm(sequences = seqs, states = states, type.sojourn = "fi", distr = distr.vec)

class(est)

plot(x = est, i = "a", col = "blue", pch = "+")