Simulate a hidden semi-Markov series and its underlying states with covariates where the latent state distributions have accelerated failure time structure whose base densities are exponential
hsmmfit_exp(y, M, trunc, dtrate, dtparm, prior, zeroparm, emitparm, tpmparm,
dt_x, zeroinfl_x, emit_x, tpm_x, yceil = NULL, method = "Nelder-Mead",
hessian = FALSE, ...)observed time series values
number of latent states
a vector specifying truncation at the maximum number of dwelling time in each state.
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 prior probabilities
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 matrix of covariates for the latent state durations
a matrix of covariates for the zero proportion
a matrix of covariates for the Poisson means
a matrix of covariates for the transition
a scalar defining the ceiling of y, above which the values will be truncated. Default to NULL.
method to be used for direct numeric optimization. See details in the help page for optim() function. Default to Nelder-Mead.
Logical. Should a numerically differentiated Hessian matrix be returned? Note that the hessian is for the working parameters, which are the generalized logit of prior probabilities (except for state 1), the generalized logit of the transition probability matrix(except 1st column), the logit of non-zero zero proportions, and the log of each state-dependent poisson means
Further arguments passed on to the optimization methods
the maximum likelihood estimates of the zero-inflated hidden Markov model
Walter Zucchini, Iain L. MacDonald, Roland Langrock. Hidden Markov Models for Time Series: An Introduction Using R, Second Edition. Chapman & Hall/CRC