Description Usage Arguments Value References Examples
Viterbi algorithm to decode the latent states for hidden semi-Markov models
1 2 3 |
y |
the observed series to be decoded |
ntimes |
vector specifying the lengths of individual, i.e. independent, time series. If not specified, the responses are assumed to form a single time series, i.e. ntimes=length(y) |
M |
number of latent states |
trunc |
a vector specifying truncation at the maximum number of dwelling time in each state. |
prior |
a vector of prior probability values |
dt_dist |
dwell time distribution, can only be "log", "shiftedpoisson", or "nonparametric". Default to "nonparametric". |
dt_parm |
a vector of parameter values in each dwell time distribution, which should be a vector of p's for dt_dist == "log", or a vector of theta's for dt_dist=="shiftpoisson", or a matrix whose a matrix whose i,j th element is the probability of staying in state i for duration j for dt_dist == "nonparametric". |
tpm_parm |
transition probability matrix |
emit_parm |
a vector containing means for each poisson distribution |
zero_init |
a vector containing structural zero proportions in each state |
plot |
whether a plot should be returned |
xlim |
vector specifying the minimum and maximum on the x-axis in the plot. Default to NULL. |
ylim |
vector specifying the minimum and maximum on the y-axis in the plot. Default to NULL. |
... |
further arguments to be passed to the plot() function |
the decoded series of latent 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 | ## Not run:
#3 zero-inflated poissons
prior_init <- c(0.3,0.3,0.4)
dt_init <- c(10,8,6)
emit_init <- c(10,50,100)
zeroprop <- c(0.5,0.3,0.2)
trunc <- c(10,10,10)
omega <- matrix(c(0,0.3,0.7,0.4,0,0.6,0.5,0.5,0),3,3,byrow=TRUE)
result <- hsmmsim(n=1000,M=3,prior=prior_init,dt_dist="shiftpoisson",
dt_parm=dt_init, tpm_parm=omega,emit_parm=emit_init,zeroprop=zeroprop)
y <- result$series
state <- result$state
fit <- hsmmfit(y=y,ntimes=NULL,M=3,trunc=trunc,prior_init=prior_init,dt_dist="shiftpoisson",
dt_init=dt_init,tpm_init=omega,emit_init=emit_init,zero_init=zeroprop,
method="Nelder-Mead",hessian=FALSE,control=list(maxit=500,trace=1))
decode <- hsmmviterbi(y=y,ntimes=NULL,M=3,trunc=trunc,prior=fit$prior,dt_dist="shiftpoisson",
dt_parm=fit$dt_parm,tpm_parm=fit$tpm,emit_parm=fit$emit_parm,
zero_init=fit$zeroprop,plot=TRUE,xlim=c(0,1000),ylim=c(0,200))
#check the missclassification rate
sum(decode!=state)/length(state)
## End(Not run)
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