Simulate a hidden semi-Markov series and its underlying states with covariates
hsmmsim2_exp(prior, dtrate, dtparm, zeroparm, emitparm, tpmparm, trunc, M, n,
dt_x = NULL, tpm_x = NULL, emit_x = NULL, zeroinfl_x = NULL)a vector of prior probabilities
a vector for the scale parameters in the base exponential density for the latent state durations.
a matrix of coefficients for the accelerated failure time model in each latent state
a vector of regression coefficients for the structural zero proportion in state 1
a matrix of regression coefficients for the Poisson regression in each state
a vector of coefficients for the multinomial logistic regression in the transition probabilities
a vector
number of latent states
length of the simulated series
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.
matrix of covariates for transition probability matrix (excluding the 1st column). Default to NULL.
matrix of covariates for the log poisson means. Default to NULL.
matrix of covariates for the nonzero structural zero proportions. Default to NULL.
simulated series and corresponding states
Walter Zucchini, Iain L. MacDonald, Roland Langrock. Hidden Markov Models for Time Series: An Introduction Using R, Second Edition. Chapman & Hall/CRC