Learn R Programming

markovchain (version 0.9.5)

markovchain-package: Easy Handling Discrete Time Markov Chains

Description

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

Arguments

Author

Giorgio Alfredo Spedicato Maintainer: Giorgio Alfredo Spedicato <spedicato_giorgio@yahoo.it>

References

Discrete-Time Markov Models, Bremaud, Springer 1999

Examples

Run this code
# 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
steadyStates(mcB)
absorbingStates(mcB)
markovchainSequence(n=20, markovchain=mcC, include=TRUE)

Run the code above in your browser using DataLab