The package contains classes and method to create and manage (plot, print, export for example) discrete time Markov chains (DTMC). In addition it provide functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of DTMC proprieties) analysis
Author
Giorgio Alfredo Spedicato Maintainer: Giorgio Alfredo Spedicato spedicato_giorgio@yahoo.it
Examples
# create some markov chains
statesNames=c("a","b")
mcA<-new("markovchain", transitionMatrix=matrix(c(0.7,0.3,0.1,0.9),byrow=TRUE,
nrow=2, dimnames=list(statesNames,statesNames)))
statesNames=c("a","b","c")
mcB<-new("markovchain", states=statesNames, transitionMatrix=
matrix(c(0.2,0.5,0.3,0,1,0,0.1,0.8,0.1), nrow=3,
byrow=TRUE, dimnames=list(statesNames, statesNames)))
statesNames=c("a","b","c","d")
matrice<-matrix(c(0.25,0.75,0,0,0.4,0.6,0,0,0,0,0.1,0.9,0,0,0.7,0.3), nrow=4, byrow=TRUE)
mcC<-new("markovchain", states=statesNames, transitionMatrix=matrice)
mcD<-new("markovchain", transitionMatrix=matrix(c(0,1,0,1), nrow=2,byrow=TRUE))
#operations with S4 methods
mcA^2
#> Unnamed Markov chain^2
#> A 2 - dimensional discrete Markov Chain defined by the following states:
#> a, b
#> The transition matrix (by rows) is defined as follows:
#> a b
#> a 0.52 0.48
#> b 0.16 0.84
#>
steadyStates(mcB)
#> a b c
#> [1,] 0 1 0
absorbingStates(mcB)
#> [1] "b"
markovchainSequence(n=20, markovchain=mcC, include=TRUE)
#> [1] "a" "b" "b" "b" "a" "b" "b" "b" "b" "b" "a" "b" "a" "b" "a" "b" "a" "b" "a"
#> [20] "b" "b"