depmixS4 (version 1.5-0)

depmixS4-package: depmixS4 provides classes for specifying and fitting hidden Markov models

Description

depmixS4 is a framework for specifying and fitting dependent mixture models, otherwise known as hidden or latent Markov models. Optimization is done with the EM algorithm or optionally with Rdonlp2 when (general linear (in-)equality) constraints on the parameters need to be incorporated. Models can be fitted on (multiple) sets of observations. The response densities for each state may be chosen from the GLM family, or a multinomial. User defined response densities are easy to add; for the latter an example is given for the ex-gauss distribution as well as the multivariate normal distribution.

Mixture or latent class (regression) models can also be fitted; these are the limit case in which the length of observed time series is 1 for all cases.

Details

Model fitting is done in two steps; first, models are specified through the depmix function (or the mix function for mixture and latent class models), which both use standard glm style arguments to specify the observed distributions; second, the model needs to be fitted by using the fit function; imposing constraints is done through the fit function. Standard output includes the optimized parameters and the posterior densities for the states and the optimal state sequence.

For full control and the possibility to add new response distributions, check the makeDepmix help page.

References

Ingmar Visser and Maarten Speekenbrink (2010). depmixS4: An R Package for Hidden Markov Models. Journal of Statistical Software, 36(7), p. 1-21.

On hidden Markov models: Lawrence R. Rabiner (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE, 77-2, p. 267-295.

On latent class models: A. L. McCutcheon (1987). Latent class analysis. Sage Publications.

See Also

depmix, fit

Examples

# NOT RUN {
	# create a 2 state model with one continuous and one binary response
	data(speed)
	mod <- depmix(list(rt~1,corr~1),data=speed,nstates=2,family=list(gaussian(),multinomial()))
	# print the model, formulae and parameter values (ie the starting values)
	mod
# }