ziphsmm (version 2.0.6)

hmmsim2: Simulate a hidden Markov series and its underlying states with covariates

Description

Simulate a hidden Markov series and its underlying states with covariates

Usage

hmmsim2(workparm, M, n, zeroindex, prior_x = NULL, tpm_x = NULL,
  emit_x = NULL, zeroinfl_x = NULL)

Arguments

workparm

working parameters

M

number of latent states

n

length of the simulated series

zeroindex

a vector specifying whether a certain state is zero-inflated

prior_x

matrix of covariates for generalized logit of prior probabilites (excluding the 1st probability). Default to NULL.

tpm_x

matrix of covariates for transition probability matrix (excluding the 1st column). Default to NULL.

emit_x

matrix of covariates for the log poisson means. Default to NULL.

zeroinfl_x

matrix of covariates for the nonzero structural zero proportions. Default to NULL.

Value

a matrix with 1st column of simulated series and 2nd column of corresponding 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 {
priorparm <- 0
tpmparm <- c(0,-0.5,0.5,0,-0.2,0.8)
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)

designx <- matrix(rnorm(2000),nrow=1000,ncol=2)
result <- hmmsim2(workparm,2,1000,zeroindex,tpm_x=designx,
      emit_x=designx,zeroinfl_x=designx)

y <- result$series

prior_init <- c(0.5,0.5)
emit_init <- c(10,30)
zero_init <- c(0.6,0)
omega <- matrix(c(0.9,0.1,0.2,0.8),2,2,byrow=TRUE)


fit <-  hmmfit(y,NULL,2,prior_init,omega,
     emit_init,zero_init, emit_x=designx,zeroinfl_x=designx,
     tpm_x=designx,hessian=FALSE,
     method="Nelder-Mead", control=list(maxit=2000,trace=1))

decode <- hmmviterbi2(y,NULL,2,fit$working_parameters,zero_init=c(1,0),
            emit_x=designx,zeroinfl_x=designx, tpm_x=designx,
            plot=TRUE, xlab="time", ylab="count",
            xlim=c(0,360),ylim=c(0,200))

sum(decode!=result$state)
# }
# NOT RUN {
# }