# hmmsmooth.cont3: 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 with covariates in the state-dependent parameters and transition rates

## Usage

 `1` ```hmmsmooth.cont3(y, x, M, prior, tpmparm, zeroparm, emitparm, timeindex) ```

## Arguments

 `y` the observed series to be decoded `x` matrix of covariates in the state-dependent parameters and transition rates. `M` number of latent states `prior` prior parameters from the fitted continuous-time hidden Markov model `tpmparm` parameters from the fitted continuous-time hidden Markov model `zeroparm` parameters for the structural zero proportions in the fitted continuous-time hidden Markov model `emitparm` parameters for the Poisson means in the fitted continuous-time hidden Markov model `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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23``` ```## Not run: set.seed(2910) priorparm <- 0 tpmparm <- c(-2,0.1,-0.1,-2,-0.2,0.2) zeroindex <- c(1,0) zeroparm <- c(0,-1,1) emitparm <- c(2,0.5,-0.5,3,0.3,-0.2) workparm <- c(priorparm,tpmparm,zeroparm,emitparm) timeindex <- rep(1,1000) for(i in 2:1000) timeindex[i] <- timeindex[i-1] + sample(1:4,1) designx <- matrix(rnorm(2000),nrow=1000,ncol=2) result <- hmmsim3.cont(workparm,2,1000,zeroindex,x=designx,timeindex=timeindex) y <- result\$series state <- result\$state fit2 <- fasthmmfit.cont3(y=y,x=designx,M=2, initparm=workparm, timeindex=timeindex, hessian=FALSE, method="CG", control=list(trace=1)) post <- hmmsmooth.cont3(y,designx,2,fit2\$prior,fit2\$tpm,fit2\$zeroparm, fit2\$emitparm,timeindex) ## End(Not run) ```

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