Sorry, I haven't the time at the moment to delve into this in detail,
but at a quick reading, your question makes no sense. If you are trying
to simulate a hidden Markov chain, the states have a distribution as
determined by the transition probability matrix and
the initial state probability distribution.
I have no idea what you mean by "state "0" to come from
Poisson and state "1" from NB distributions". As far as I can discern
this is complete gibberish.
Good luck.
On 05/06/14 08:22, Bukar Alhaji wrote:
Dear Dr Rolf,
Thank you for your response and suggestion. But i still find it very
difficult to relate my own problem to the sim.hmm() function you
suggested. Because the states and the observations should come from
Poisson and Negative Binomial distributions where state "0" to come from
Poisson and state "1" from NB distributions. However, i run the code and
have the states and the observations but not sure if it has been done
correctly.
Please can you explain how i should make use of sim.hmm() function to
apply to my case, or can improvement be made on my code?
Thank you so much for givig me your time.
Regards
Zakir
On Tuesday, June 3, 2014 11:32 PM, Rolf Turner <r.tur...@auckland.ac.nz>
wrote:
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)
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