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, using the forward-backward algorithm.
viterbi.msm(x, normboot = FALSE, newdata = NULL)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.
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.
An optional data frame containing observations on which to
construct the Viterbi path and forward-backward probabilities. It must be in
the same format as the data frame used to fit x. If NULL, the
data frame used to fit x is used.
C. H. Jackson chris.jackson@mrc-bsu.cam.ac.uk
Durbin, R., Eddy, S., Krogh, A. and Mitchison, G. Biological sequence analysis, Cambridge University Press, 1998.
msm