Description Usage Arguments Value References Examples
View source: R/hsmmviterbi_exp.R
Viterbi algorithm to decode the latent states in hidden semi-Markov models with covariates where the latent state durations have accelerated failure time structure
1 2 3 |
y |
the observed series to be decoded |
M |
number of latent states |
trunc |
a vector specifying the truncation at the maximum number of dwelling time in each state. |
dtrate |
a vector for the scale parameters in the base exponential density for the latent state durations. |
dtparm |
a matrix of coefficients for the accelerated failure time model in each latent state |
prior |
a vector of prior probabilities |
zeroparm |
a vector of regression coefficients for the structural zero proportion in state 1 |
emitparm |
a matrix of regression coefficients for the Poisson regression in each state |
tpmparm |
a vector of coefficients for the multinomial logistic regression in the transition probabilities |
dt_x |
a matrix of covariates for the latent state durations |
zeroinfl_x |
a matrix of covariates for the zero proportion |
emit_x |
a matrix of covariates for the Poisson means |
tpm_x |
a matrix of covariates for the transition |
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 |
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 24 25 26 27 28 | ## Not run:
M <- 3
prior <- c(0.5,0.3,0.2)
dtrate <- c(6,5,4)
dtparm <- matrix(c(0.2,0.1,0.2),nrow=3)
zeroparm <- c(0,-0.2)
emitparm <- matrix(c(4,0.3,5,0.2,6,-0.1),3,2,byrow=TRUE)
tpmparm <- c(1,0.2,0.5,-0.2,0,0.2)
emit_x <- matrix(c(rep(1,1000),rep(0,1000)),nrow=2000,ncol=1)
dt_x <- emit_x
tpm_x <- emit_x
zeroinfl_x <- emit_x
trunc <- c(18,15,10)
re <- hsmmsim2_exp(prior,dtrate,dtparm,zeroparm,emitparm,tpmparm,
trunc, M, n, dt_x,tpm_x, emit_x, zeroinfl_x)
y <- re$series
rrr <- hsmmfit_exp(y,M,trunc,dtrate,dtparm,prior,zeroparm,emitparm,tpmparm,
dt_x,zeroinfl_x,emit_x,tpm_x,method="BFGS",control=list(trace=1))
decode <- hsmmviterbi_exp(y,M, trunc,dtrate,dtparm,
prior,zeroparm,emitparm,tpmparm,
dt_x, zeroinfl_x, emit_x, tpm_x)
sum(decode!=re$state)
## End(Not run)
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