Description Usage Arguments Value References Examples
View source: R/hmmviterbi.cont.R
Viterbi algorithm to decode the latent states for continuous-time hidden Markov models without covariates
1 2 |
y |
the observed series to be decoded |
M |
number of latent states |
prior_init |
a vector of prior probability values |
tpm_init |
transition rate matrix |
emit_init |
a vector containing means for each poisson distribution |
zero_init |
a vector containing structural zero proportions in each state |
timeindex |
a vector containing the time points |
plot |
whether a plot should be returned |
xlim |
vector specifying the minimum and maximum on the x-axis in the plot. Default to NULL. |
ylim |
vector specifying the minimum and maximum on the y-axis in the plot. Default to NULL. |
... |
further arguments to be passed to the plot() function |
the decoded series of latent states
Walter Zucchini, Iain L. MacDonald, Roland Langrock. Hidden Markov Models for Time Series: An Introduction Using R, Second Edition. Chapman & Hall/CRC
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | prior_init <- c(0.5,0.2,0.3)
emit_init <- c(10,40,70)
zero_init <- c(0.5,0,0)
omega <- matrix(c(-0.3,0.2,0.1,0.1,-0.2,0.1,0.2,0.2,-0.4),3,3,byrow=TRUE)
timeindex <- rep(1,1000)
for(i in 2:1000) timeindex[i] <- timeindex[i-1] + sample(1:3,1)
result <- hmmsim.cont(n=1000,M=3,prior=prior_init, tpm_parm=omega,
emit_parm=emit_init,zeroprop=zero_init,timeindex=timeindex)
y <- result$series
fit2 <- fasthmmfit.cont(y,x=NULL,M=3,prior_init,omega,
emit_init,0.5,timeindex=timeindex,hessian=FALSE,
method="BFGS", control=list(maxit=500,trace=1))
decode2 <- hmmviterbi.cont(y,3,fit2$prior,fit2$tpm,fit2$emit,
c(fit2$zeroprop,0,0),timeindex=timeindex)
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