R: Smoothing a discrete time series.

smooth.discrete {mhsmm} | R Documentation

## Smoothing a discrete time series.

### Description

The smooth.discrete() function provides a simple smoothing of a time series of discrete values measured at equidistant times. Under the hood of smooth.discrete() is a hidden Markov model.

### Usage

    smooth.discrete(y, init = NULL, trans = NULL, parms.emission = 0.5,
                    method = "viterbi", details = 0, ...)

### Arguments

`y` | A numeric vector
`init` | Initial distribution (by default derived from data; see the vignette for details)
`trans` | Transition matrix (by default derived from data; see the vignette for details)
`parms.emission` | Matrix describing the conditional probabilities of the observed states given the latent states. (See the vignette for details).
`method` | Either "viterbi" or "smoothed". The viterbi method gives the jointly most likely sequence; the smoothed method gives the sequence of individually most likely states.
`details` | Controlling the amount of information printed.
`...` | Further arguments passed on to the "hmmfit" function.

### Details

The parameters are estimated using the Baum-Welch algorithm (a special case of the EM-algorithm).

### Value

A list with the following components:

`s` | The "smoothed" states
`model` | The underlying hmm (hidden Markov model) object
`data` | The data
`initial` | The initial parameters

### Author(s)

Søren Højsgaard <sorenh at agrsci.dk>

### See Also

`hmmspec`, `hmmfit`

### Examples

    ## Please see the vignette

[Package mhsmm version 0.4.16 Index]