hsmmspec: Hidden semi-Markov model specification
In jaredo/mhsmm: Inference for Hidden Markov and Semi-Markov Models

Description Usage Arguments Details Value Author(s) References See Also

Creates a model specification of a hidden semi-Markov model.

1 2	hsmmspec(init,transition,parms.emission,sojourn,dens.emission, rand.emission=NULL,mstep=NULL)

`init`	Distribution of states at t=1 ie. P(S=s) at t=1
`transition`	The transition matrix of the embedded Markov chain (diagonal must be 0)
`parms.emission`	A list containing the parameters of the emission distribution
`sojourn`	A list containining the parameters and type of sojourn distribtuion (see Details)
`dens.emission`	Density function of the emission distribution
`rand.emission`	The function used to generate observations from the emission distribution
`mstep`	Re-estimates the parameters of density function on each iteration

The sojourn argument provides a list containing the parameters for the available sojourn distributions. Available sojourn distributions are shifted Poisson, Gamma and non-parametric.

In the case of the Gamma distribution, sojourn is a list with vectors shape and scale (the Gamma parameters in dgamma), both of length J. Where J is the number of states. See reprocows for an example using Gamma sojourn distributions.

In the case of shifted Poisson, sojourn is list with vectors shift and lambda, both of length J. See hsmmfit for an example using shifted Poisson sojourn distributions.

In the case of non-parametric, sojourn is a list containing a M x J matrix. Where entry (i,j) is the probability of a sojourn of length i in state j. See hsmmfit for an example using shifted non-parametric sojourn distributions.

An object of class hsmmspec

Jared O'Connell jaredoconnell@gmail.com

Jared O'Connell, Soren Hojsgaard (2011). Hidden Semi Markov Models for Multiple Observation Sequences: The mhsmm Package for R., Journal of Statistical Software, 39(4), 1-22., URL http://www.jstatsoft.org/v39/i04/.

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

hsmmfit ,simulate.hsmmspec, predict.hsmm

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