## create two state HMM with t distributions
state.names <- c("one","two")
transition <- c(0.1, 0.02)
location <- c(1, 2)
scale <- c(1, 1)
df <- c(4, 6)
model <- getHMM(list(a=transition, mu=location, sigma=scale, nu=df),
state.names)
## obtain observation sequence from model
obs <- sampleSeq(model, 100)
## calculate posterior probability for state "one"
post <- posterior(obs, model, log=FALSE)
## get sequence of individually most likely states
state.seq <- apply(post,2,max)
state.seq <- states(model)[state.seq]
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