predict.hsmm: Prediction for hsmms

In jaredo/mhsmm: Inference for Hidden Markov and Semi-Markov Models

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 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 method="smoothed")

Author(s)

Jared O'Connell [e-mail]

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

jaredo/mhsmm documentation built on Dec. 6, 2019, 11:07 a.m.