hmmsmooth.cont: Compute the posterior state probabilities for continuous-time...

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

Description

Compute the posterior state probabilities for continuous-time hidden Markov models without covariates where zero-inflation only happens in state 1

Usage

hmmsmooth.cont(y, M, prior_init, tpm_init, emit_init, zero_init, timeindex)

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 scalar containing structural zero proportion in state 1
timeindex	a vector containing the time points

Value

posterior state probabilities

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$series
fit2 <-  fasthmmfit.cont(y,x=NULL,M=3,prior_init,omega,
                        emit_init,0.5,timeindex=timeindex,hessian=FALSE,
                        method="BFGS", control=list(maxit=500,trace=1))
post <- hmmsmooth.cont(y,3,fit2$prior,fit2$tpm,fit2$emit,
           fit2$zeroprop,timeindex=timeindex)

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