ziphsmm (version 2.0.6)

hmmviterbi.cont: Viterbi algorithm to decode the latent states for continuous-time hidden Markov models without covariates

Description

Viterbi algorithm to decode the latent states for continuous-time hidden Markov models without covariates

Usage

hmmviterbi.cont(y, M, prior_init, tpm_init, emit_init, zero_init, timeindex,
  plot = FALSE, xlim = NULL, ylim = NULL, ...)

Arguments

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

Value

the decoded series of latent states

References

Walter Zucchini, Iain L. MacDonald, Roland Langrock. Hidden Markov Models for Time Series: An Introduction Using R, Second Edition. Chapman & Hall/CRC

Examples

# NOT RUN {
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)    

# }