R: Prediction for hsmms

predict.hsmm {mhsmm} | R Documentation

## Prediction for hsmms

### Description

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

### Usage

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

### Arguments

`object` | An object of type `hsmm`
`newdata` | A vector or dataframe 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 HSMMs, 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

Guedon, Y. (2003), Estimating hidden semi-Markov chains from discrete sequences, Journal of Computational and Graphical Statistics, Volume 12, Number 3, page 604-639 - 2003

### See Also

`hsmmfit`,`predict.hsmmspec`

### Examples

    ##See 'hsmmfit' for examples

[Package mhsmm version 0.4.16 Index]