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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