rmvnorm.hsmm: Random number generation from a multivariate normal...

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

Random number generation from a multivariate normal distributed emission distribution

Description

This generates values from a multivariate normal distributed emission state j given parameters in model.

Usage

rmvnorm.hsmm(j, model)

Arguments

j	An integer representing the state
model	A hmmspec or hsmmspec object

Details

This is essentially a wrapper for rnorm. Users may build functions with the same arguments and return values so they can use their own custom emission distributions.

Value

A single value from the emission distribution.

Author(s)

Jared O'Connell [e-mail]

See Also

dmvnorm.hsmm, mstep.mvnorm

Examples

  J<-2
  initial <- rep(1/J,J)
  P <- matrix(c(.3,.5,.7,.5),nrow=J)
  b <- list(mu=list(c(-3,0),c(1,2)),sigma=list(diag(2),matrix(c(4,2,2,3), ncol=2)))
  model <- hmmspec(init=initial, trans=P, parms.emission=b,dens.emission=dmvnorm.hsmm)
  model
  train <- simulate(model, nsim=300, seed=1234, rand.emis=rmvnorm.hsmm)
  plot(train,xlim=c(0,100))
  h1 = hmmfit(train,model,mstep=mstep.mvnorm)

mhsmm documentation built on Aug. 23, 2023, 9:06 a.m.