ziphsmm (version 2.0.6)

hmmviterbi2: Viterbi algorithm to decode the latent states in hidden Markov models with covariate values

Description

Viterbi algorithm to decode the latent states in hidden Markov models with covariate values

Usage

hmmviterbi2(y, ntimes = NULL, M, workparm, zero_init, prior_x = NULL,
  tpm_x = NULL, emit_x = NULL, zeroinfl_x = NULL, plot = FALSE,
  xlim = NULL, ylim = NULL, ...)

Arguments

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

workparm

a vector of values for working parameters, which is the last element returned from hmmfit() function. This consists the generalized logit of prior probabilities (except for the 1st state), generalized logit of transition probability matrix (except for the 1st column), the logit of nonzero structural zero proportions, and the log poisson means

zero_init

a vector containing structural zero proportions in each state, e.g. set zero_init[i] to be 0 if the i-th state is a regular poisson, and otherwise 1.

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.

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

Value

decoded series of latent 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 {
data(CAT)
y <- CAT$activity
x <- data.matrix(CAT$night)
prior_init <- c(0.5,0.2,0.3)
emit_init <- c(10,50,100)
zero_init <- c(0.5,0,0)
omega <- matrix(c(0.5,0.3,0.2,0.4,0.3,0.3,0.2,0.4,0.4),3,3,byrow=TRUE)
fit2 <-  hmmfit(y,rep(1440,3),3,prior_init,omega,
     emit_init,zero_init, emit_x=x,zeroinfl_x=x,hessian=FALSE,
     method="Nelder-Mead", control=list(maxit=500,trace=1))
decode <- hmmviterbi2(y,rep(1440,3),3,fit2$working_parameters,zero_init=c(1,0,0),
            emit_x=x,zeroinfl_x=x, plot=TRUE, xlab="time", ylab="count",
            xlim=c(0,360),ylim=c(0,200))


# }