Getting Started with Markov Chains

There are number of R packages devoted to sophisticated applications of Markov chains. These include msm and SemiMarkov for fitting multistate models to panel data,mstate for survival analysis applications, TPmsm for estimating transition probabilities for 3-state progressive disease models, heemod for applying Markov models to health care economic applications, HMM and depmixS4 for fitting Hidden Markov Modelsandmcmc for working with Monte Carlo Markov Chains. All of these assume some considerable knowledge of the underlying theory. To my knowledge only DTMCPackand the relatively recent package,  markovchain, were written to facilitate basic computations with Markov chains.

In this post, we’ll explore some basic properties of discrete time Markov chains using the functions provided by the markovchain package supplemented with standard R functions and a few functions from other contributed packages. “Chapter 11”, of Snell’s online probability book will be our guide. The calculations displayed here illustrate some of the theory developed in this document. In the text below, section numbers refer to this document.

A large part of working with discrete time Markov chains involves manipulating the matrix of transition probabilities associated with the chain.  This first section of code replicates the Oz transition probability matrix from section 11.1 and uses theplotmat() function from the diagram package to illustrate it. Then, the efficient operator %^% from the expm package is used to raise the Oz matrix to the third power. Finally, left matrix multiplication of OZ^3 by the distribution vector u = (1/3, 1/3, 1/3) gives the weather forecast three days ahead.


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