ziphsmm (version 2.0.6)

hsmmsim2: Simulate a hidden semi-Markov series and its underlying states with covariates

Description

Simulate a hidden semi-Markov series and its underlying states with covariates

Usage

hsmmsim2(workparm, M, n, zeroindex, dt_dist = "nonparametric", dt_x = NULL,
  prior_x = NULL, tpm_x = NULL, emit_x = NULL, zeroinfl_x = NULL)

Arguments

workparm

working parameters. The first part is the logit of parameter p for each log-series dwell time distribution or the log of parameter theta for each shifted-poisson dwell time distribution. If dt_dist is "nonparametric", then the first part is empty. The next part is the generalized logit of prior probabilities (except for the 1st state),the logit of each nonzero structural zero proportions, the log of each state-dependent poisson means, and the generalized logit of the transition probability matrix(except 1st column and the diagonal elements).

M

number of latent states

n

length of the simulated series

zeroindex

a vector specifying whether a certain state is zero-inflated

dt_dist

dwell time distribution, can only be "log" or "shiftedpoisson" or "nonparametric". Default to "nonparametric".

dt_x

if dt_dist is "nonparametric", then dt_x is the matrix of nonparametric state durataion probabilities. Otherwise, dt_x is matrix of covariates for the dwell time distribution parameters in log-series or shifted-poisson distributions.Default to NULL.

prior_x

matrix of covariates for generalized logit of prior probabilites (excluding the 1st probability). Default to NULL. Set to NULL if dt_dist is "nonparametric".

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

simulated series and 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 {
#example 1

dtparm <- c(2,1)  #in the log scale
priorparm <- 0
zeroindex <- c(1,0)
zeroparm <- c(0,-1,1)
emitparm <- c(2,0.5,-0.5,4,0.3,-0.2)
workparm <- c(dtparm,priorparm,zeroparm,emitparm)

designx <- matrix(rnorm(4000),nrow=2000,ncol=2)
result <- hsmmsim2(workparm,2,2000,zeroindex,"shiftpoisson",
      emit_x=designx,zeroinfl_x=designx)

y <- result$series

dt_init <- c(8,3)
prior_init <- c(0.5,0.5)
emit_init <- c(10,50)
trunc <- c(13,8)
zeroprop <- c(0.8,0)
omega <- matrix(c(0,1,1,0),2,2,byrow=TRUE)


fit1 <- hsmmfit(y=y,M=2,trunc=trunc,prior_init=prior_init,dt_dist="shiftpoisson",
     dt_init=dt_init,emit_x=designx,zeroinfl_x=designx,
     tpm_init=omega,emit_init=emit_init,zero_init=zeroprop,
     method="Nelder-Mead",hessian=FALSE,control=list(maxit=2000,trace=1))

decode <- hsmmviterbi2(y,NULL,2,trunc,fit1$working_parameters,
            dt_dist="shiftpoisson", zero_init=zeroprop,
            emit_x=designx,zeroinfl_x=designx, plot=TRUE, xlab="time", ylab="count",
            xlim=c(0,2000),ylim=c(0,200))
sum(decode!=result$state)



#example 2

dtparm <- c(2,0,-1)  #logit scale
priorparm <- c(0,0)
zeroindex <- c(1,0,0)
zeroparm <- c(0,-1,1)
emitparm <- c(2,0.5,-0.5,4,0.3,-0.2,6,0.2,-0.2)
tpmparm <- c(0,0,0)
workparm <- c(dtparm,priorparm,zeroparm,emitparm,tpmparm)

designx <- matrix(rnorm(4000),nrow=2000,ncol=2)
result <- hsmmsim2(workparm,3,2000,zeroindex,"log",
      emit_x=designx,zeroinfl_x=designx)

y <- result$series

dt_init <- c(0.9,0.5,0.3)
prior_init <- c(0.3,0.4,0.3)
emit_init <- c(10,100,400)
trunc <- c(13,5,3)
zeroprop <- c(0.8,0,0)
omega <- matrix(c(0,0.5,0.5,0.5,0,0.5,0.5,0.5,0),3,3,byrow=TRUE)


fit2 <- hsmmfit(y=y,M=3,trunc=trunc,prior_init=prior_init,dt_dist="log",
     dt_init=dt_init,emit_x=designx,zeroinfl_x=designx,
     tpm_init=omega,emit_init=emit_init,zero_init=zeroprop,
     method="Nelder-Mead",hessian=FALSE,control=list(maxit=2000,trace=1))

decode <- hsmmviterbi2(y,NULL,3,trunc,fit2$working_parameters,
            dt_dist="shiftpoisson", zero_init=zeroprop,
            emit_x=designx,zeroinfl_x=designx, plot=TRUE, xlab="time", ylab="count",
            xlim=c(0,2000),ylim=c(0,1000))
sum(decode!=result$state)
# }
# NOT RUN {
# }