MClik: Likelihood Estimation for Markov Chains

In SMPracticals: Practicals for Use with Davison (2003) Statistical Models

Description

Computes maximum likelihood estimates of transition probabilities for stationary Markov chain models, of order 0 (independence) to 3.

This is intended for use with Practical 6.1 of Davison (2003), not as production code.

Usage

MClik(d)

Arguments

d	A sequence containing successive states of the chain

Value

order	order of fitted chain
df	degrees of freedom using in fitting
L	maximum log likelihood for each order
AIC	Akaike information criterion for each order
one	one-way marginal table of counts
two	two-way margin table of transitions
three	three-way marginal table of transitions
four	four-way marginal table of transitions

Author(s)

A. C. Davison ([e-mail])

References

Avery, P. J. and Henderson, D. A. (1999) Fitting Markov chain models to discrete state series such as DNA sequences. Applied Statistics, 48, 53–61.

Davison, A. C. (2003) Statistical Models. Cambridge University Press. Section 6.1.

Examples

data(intron)

fit <- MClik(intron)

Example output

Loading required package: ellipse

Attaching package: ‘ellipse’

The following object is masked from ‘package:graphics’:

    pairs

SMPracticals documentation built on May 2, 2019, 11:12 a.m.