hmm.discnp (version 0.2-4)

viterbi: Most probable state sequence.

Description

Calculates ``the'' most probable state sequence underlying each of one or more replicate observation sequences.

Usage

viterbi(y, object = NULL, tpm, Rho, ispd=NULL,log=FALSE)

Arguments

y

The observations for which the underlying most probable hidden states are required. May be a sequence of observations, or a list each entry of which constitutes an independent sequence of observations. If y is missing (and if object is not NULL) then y is extracted from object, provided that the y component of object is present. Otherwise an error is given.

object

An object describing a hidden Markov model, as fitted to the data set y by hmm().

tpm

The transition probability matrix for a hidden Markov model; ignored if object is non-null.

Rho

A matrix specifying the probability distributions of the observations for a hidden Markov model; ignored if object is non-null. Should bear some reasonable relationship to y. If Rho has row names then these must include all values of the observations. If Rho does not have row names then the values of the observations must be integers between 1 and nrow(Rho).

ispd

The initial state probability distribution for a hidden Markov model; ignored if object is non-null. Should bear some reasonable relationship to y. If object and ispd are both NULL then ispd is set equal to the stationary distribution calculated from tpm.

log

Logical scalar. Should logarithms be used in the recursive calculations of the probabilities involved in the Viterbi algorithm, so as to avoid underflow? If log is FALSE then underflow is avoided instead by a normalization procedure. The quantity delta (see Rabiner 1989, page 264) is replaced by delta/sum(delta) at each step. It should actually make no difference whether log is set to TRUE. I just included the option because I could. Also the HMM package uses the logarithm approach so setting log=TRUE might be of interest if comparisons are to be made between the results of the two packages.

Value

If y consists of a single observation sequence, the value is the underlying most probable observation sequence, or a matrix whose columns consist of such sequences if there is more than one (equally) most probable sequence.

If y consists of a list of observation sequences, the value is a list each entry of which is of the form described above.

Warning

There may be more than one equally most probable state sequence underlying a given observation sequence. This phenomenon appears to be unlikely to occur in practice.

Thanks

The correction made to the code so as to avoid underflow problems was made due to an inquiry and suggestion from Owen Marshall.

Details

Applies the Viterbi algorithm to calculate ``the'' most probable robable state sequence underlying each observation sequences.

References

Rabiner, L. R., "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE vol. 77, pp. 257 -- 286, 1989.

See Also

hmm(), sim.hmm(), mps(), pr(), viterbi()

Examples

# NOT RUN {
# See the help for sim.hmm() for how to generate y.num and y.let.
# }
# NOT RUN {
fit.num <- hmm(y.num,K=2,verb=TRUE)
v.1 <- viterbi(object=fit.num)
v.2 <- viterbi(y.num,tpm=P,Rho=R) # P and R as in the
                                  # help for sim.hmm().
# The order of the states has gotten swapped; 3-v.1[[1]] is much
# more similar to v.2[[1]] than is v.1[[1]].

fit.let <- hmm(y.let,K=2,verb=TRUE)
v.3 <- viterbi(object=fit.let)    # Works.
v.4 <- viterbi(y.let,tpm=P,Rho=R) # Throws an error (R has no row names.)
# }

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