Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/hmm_functions.R
Predicts the underlying state sequence for an observed sequence newdata
given a hmm
model
1 2 |
object |
An object of class |
newdata |
A vector or list of observations |
method |
Prediction method (see details) |
... |
further arguments passed to or from other methods. |
If method="viterbi"
, this technique applies the Viterbi algorithm for HMMs, producing the most likely sequence of states given the observed data. If method="smoothed"
, then the individually most likely (or smoothed) state sequence is produced, along with a matrix with the respective probabilities for each state.
Returns a hsmm.data
object, suitable for plotting.
newdata |
A vector or list of observations |
s |
A vector containing the reconstructed state sequence |
N |
The lengths of each sequence |
p |
A matrix where the rows represent time steps and the columns are the probability for the respective state (only produced when |
Jared O'Connell jaredoconnell@gmail.com
Rabiner, L. (1989), A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 77, 257-286.
1 | ##See examples in 'hmmfit'
|
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