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.
A fitted hidden Markov multi-state model, or a model with
censored state observations, as produced by
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.
C. H. Jackson email@example.com
Durbin, R., Eddy, S., Krogh, A. and Mitchison, G. Biological sequence analysis, Cambridge University Press, 1998.
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