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

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

Description

Viterbi algorithm to decode the latent states for hidden Markov models

Usage

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

Arguments

y	the observed series to be decoded
ntimes	vector specifying the lengths of individual, i.e. independent, time series. If not specified, the responses are assumed to form a single time series, i.e. ntimes=length(y)
M	number of latent states
prior_init	a vector of prior probability values
tpm_init	transition probability matrix
emit_init	a vector containing means for each poisson distribution
zero_init	a vector containing structural zero proportions in each state
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(0.5,0.3,0.2,0.4,0.3,0.3,0.2,0.4,0.4),3,3,byrow=TRUE)
result <- hmmsim(n=1000,M=3,prior=prior_init, tpm_parm=omega,
         emit_parm=emit_init,zeroprop=zero_init)
y <- result$series
state <- result$state
fit <- hmmfit(y=y,M=3,prior_init=prior_init,tpm_init=omega,
     emit_init=emit_init,zero_init=zero_init,
     method="Nelder-Mead",hessian=FALSE,control=list(maxit=500,trace=1))

decode <- hmmviterbi(y,NULL,3,fit$prior,fit$tpm,fit$emit_parm,fit$zeroprop,
                      plot=TRUE,xlab="Time",ylab="Count")      
#check the missclassification rate
sum(decode!=state)/length(state)      

## Not run: 
decode <- hmmviterbi(y,NULL,3,fit$prior,fit$tpm,fit$emit_parm,fit$zeroprop,
                      plot=TRUE,xlim=c(0,100),ylim=c(0,100),
                      xlab="Time",ylab="Count")  

## End(Not run)

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