hsmmsim2_exp: Simulate a hidden semi-Markov series and its underlying...

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

Description

Simulate a hidden semi-Markov series and its underlying states with covariates

Usage

hsmmsim2_exp(prior, dtrate, dtparm, zeroparm, emitparm, tpmparm, trunc, M, n,
  dt_x = NULL, tpm_x = NULL, emit_x = NULL, zeroinfl_x = NULL)

Arguments

prior	a vector of prior probabilities
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
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
trunc	a vector
M	number of latent states
n	length of the simulated series
dt_x	if dt_dist is "nonparametric", then dt_x is the matrix of nonparametric state durataion probabilities. Otherwise, dt_x is matrix of covariates for the dwell time distribution parameters in log-series or shifted-poisson distributions.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.

Value

simulated series and corresponding states

References

Walter Zucchini, Iain L. MacDonald, Roland Langrock. Hidden Markov Models for Time Series: An Introduction Using R, Second Edition. Chapman & Hall/CRC

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