hmmviterbi.cont: Viterbi algorithm to decode the latent states for...

In ziphsmm: Zero-Inflated Poisson Hidden (Semi-)Markov Models

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

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)    

ziphsmm documentation built on May 2, 2019, 6:10 a.m.