simulate.hmmspec: Simulation of hidden Markov models

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

Description

Simulates data from a hidden Markov model

Usage

## S3 method for class 'hmmspec'
simulate(object, nsim, seed = NULL, rand.emission=NULL,...)

Arguments

object	A hmmspec object
nsim	An integer or vector of integers (for multiple sequences) specifying the length of the sequence(s)
seed	seed for the random number generator
rand.emission	The function used to generate observations from the emission distribution
...	further arguments passed to or from other methods.

Details

If nsim is a single integer then a HMM of that length is produced. If nsim is a vector of integers, then length(nsim) sequences are generated with respective lengths.

Value

An object of class hmmdata

x	A vector of length sum(N) - the sequence(s) of observed values
s	A vector of length sum(N) - the sequence(s) of hidden states
N	A vector of the length of each observation sequence (used to segment x and s)

Author(s)

Jared O'Connell [e-mail]

References

Rabiner, L. (1989), A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 77, 257-286.

See Also

hmmspec, link{predict.hmm}

Examples

J<-3
initial <- rep(1/J,J)
P <- matrix(c(.8,.5,.1,0.05,.2,.5,.15,.3,.4),nrow=J)
b <- list(mu=c(-3,0,2),sigma=c(2,1,.5))
model <- hmmspec(init=initial, trans=P, parms.emission=b,dens.emission=dnorm.hsmm)
train <- simulate(model, nsim=100, seed=1234, rand.emis=rnorm.hsmm)
plot(train)

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