R: Prediction function for hmm

predict.hmm {mhsmm} | R Documentation

## Prediction function for hmm

### Description

Predicts the underlying state sequence for an observed sequence `newdata` given a `hmm` model

### Usage

    ## S3 method for class 'hmm'
    predict(object, newdata,method = "viterbi", ...)

### Arguments

`object` | An object of class `hmm`
`newdata` | A vector or data.frame of observations
`method` | Prediction method (see details)
`...` | further arguments passed to or from other methods.

### Details

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.

### Value

Returns a `hsmm.data` object, suitable for plotting.

`newdata` | A vector or data.frame 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 `method="smoothed"`)

### Author(s)

Jared O'Connell jaredoconnell@gmail.com

### References

Rabiner, L. (1989), A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 77, 257-286.

### See Also

`hmmfit,hmmspec`

### Examples

    ##See examples in 'hmmfit'

[Package mhsmm version 0.4.16 Index]