markovchain (version 0.0.9.5)

markovchainFit: Function to fit a discrete Markov chain

Description

Given a sequence of states arising from a stationary state, it fits the underlying Markov chain distribution using either MLE (also using a Laplacian smoother) or bootstrap.

Usage

markovchainFit(data, method = "mle", byrow = TRUE, nboot = 10,laplacian=0,name, 
parallel=FALSE)
createSequenceMatrix(stringchar, toRowProbs = FALSE, sanitize = TRUE)

Arguments

data

A character list.

method

Method used to estimate the Markov chain. Either "mle" or "bootstrap" or "laplace"

byrow

it tells whether the output Markov chain should show the transition probabilities by row.

nboot

Number of bootstrap replicates in case "bootstrap" is used.

laplacian

Laplacian smoothing parameter, default zero. It is only used when "laplace" method is chosen.

name

Optional character for name slot.

parallel

Use parallel processing when performing Boostrap estimates.

stringchar

Equivalent to data

toRowProbs

converts a sequence matrix into a probability matrix

sanitize

put 1 in all rows having rowSum equal to zero

Value

• A list containing an estimate and, when "bootstrap" method is used, a matrix of standards deviations and the bootstrap samples.

Warning

"mle" method calls createSequenceMatrix function using sanitize parameter set to TRUE.

References

A First Course in Probability (8th Edition), Sheldon Ross, Prentice Hall 2010

See Also

markovchainSequence

Examples

sequence<-c("a", "b", "a", "a", "a", "a", "b", "a", "b", "a", "b", "a", "a", 
"b", "b", "b", "a")
sequenceMatr<-createSequenceMatrix(sequence,sanitize=FALSE)
mcFitMLE<-markovchainFit(data=sequence)
mcFitBSP<-markovchainFit(data=sequence,method="bootstrap",nboot=5, name="Bootstrap Mc")