Description Usage Arguments Value References Examples
Simulate a hidden Markov series and its underlying states with covariates
1 2 |
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. |
a matrix with 1st column of simulated series and 2nd column of corresponding states
Walter Zucchini, Iain L. MacDonald, Roland Langrock. Hidden Markov Models for Time Series: An Introduction Using R, Second Edition. Chapman & Hall/CRC
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ## 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)
## End(Not run)
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