For a fitted hidden Markov model, or a model with censored state observations, the Viterbi algorithm recursively constructs the path with the highest probability through the underlying states. The probability of each hidden state is also computed for hidden Markov models.
viterbi.msm(x, normboot=FALSE)A fitted hidden Markov multi-state model, or a model with
censored state observations, as produced by msm
If TRUE, then before running the algorithm, the
maximum likelihood estimates of the model parameters are replaced by
an alternative set of parameters drawn randomly from the asymptotic
multivariate normal distribution of the MLEs.
A data frame with columns:
subject = subject identification numbers
time = times of observations
observed = corresponding observed states
fitted = corresponding fitted states found by Viterbi
recursion. If the model is not a hidden Markov model and there are
no censored state observations, this is just the observed states.
For hidden Markov models, an additional matrix pstate is also
returned inside the data frame, giving the probability of each
hidden state at each point, conditionally on all the data. This is
computed by the forward/backward algorithm.
Durbin, R., Eddy, S., Krogh, A. and Mitchison, G. Biological sequence analysis, Cambridge University Press, 1998.