R help - Jun 2014 - Code for generating states and observations for HMM

Dear R buddies, 

Sorry for this silly question but am new to R. I am trying to generate states
and observations to be use for Bayesian Hidden Markov Models analysis where i
intend using mixture of Poisson and Negative binomial as emulsion. I use the
code below to generate states and observations for homogeneous HMM . I would
like to know if i correctly generated the data. 
 
   
pii = c(0.6,0.4)
p1 <- matrix(c(0.8,0.2,0.3,0.7),byrow=TRUE,nrow=2)


    NUM = 200    
    theta<-rep(0, NUM)
    x<-rep(0, NUM)
   
    ## generating the states
    # initial state
    theta[1]<-rbinom(1, 1, pii[1])
    # other states
    for (i in 2:NUM)
    {
      if (theta[i-1]==0)
        theta[i]<-rbinom(1, 1, p1[1, 1])
      else
        theta[i]<-rbinom(1, 1, p1[2, 1])
    }
    
    ## generating the observations
    
    for (i in 1:NUM)
    {
      if (theta[i]==0)
      {
        x[i]<-rpois(1, 5)
      }
      else
      { 
        x[i]<-rnbinom(1, 3, 0.3)
      }
    }
    data<-list(s=theta, o=x, p1 = p1, pii = pii)

Thanks for your response.
Zakir

	[[alternative HTML version deleted]]

You might find it helpful to look at the sim.hmm() function in the 
"hmm.discnp" package, or the simHMM() function in the "HMM"
package.

cheers,

Rolf Turner

On 03/06/14 21:17, Bukar Alhaji wrote:
> Dear R buddies,
>
> Sorry for this silly question but am new to R. I am trying to
> generate states and observations to be use for Bayesian Hidden Markov
> Models analysis where i intend using mixture of Poisson and Negative
> binomial as emulsion. I use the code below to generate states and
> observations for homogeneous HMM . I would like to know if i
> correctly generated the data.
>
>
> pii = c(0.6,0.4)
> p1 <- matrix(c(0.8,0.2,0.3,0.7),byrow=TRUE,nrow=2)
>
>
>      NUM = 200
>      theta<-rep(0, NUM)
>      x<-rep(0, NUM)
>
>      ## generating the states
>      # initial state
>      theta[1]<-rbinom(1, 1, pii[1])
>      # other states
>      for (i in 2:NUM)
>      {
>        if (theta[i-1]==0)
>          theta[i]<-rbinom(1, 1, p1[1, 1])
>        else
>          theta[i]<-rbinom(1, 1, p1[2, 1])
>      }
>
>      ## generating the observations
>
>      for (i in 1:NUM)
>      {
>        if (theta[i]==0)
>        {
>          x[i]<-rpois(1, 5)
>        }
>        else
>        {
>          x[i]<-rnbinom(1, 3, 0.3)
>        }
>      }
>      data<-list(s=theta, o=x, p1 = p1, pii = pii)