hmmviterbi2: Viterbi algorithm to decode the latent states in hidden...

In ziphsmm: Zero-Inflated Poisson Hidden (Semi-)Markov Models

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

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))

ziphsmm documentation built on May 2, 2019, 6:10 a.m.