hsmm {hsmm} | R Documentation |
Simulating Hidden Semi-Markov Models
hsmm.sim(tau, od, rd, pi.par, tpm.par, od.par, rd.par, M = NA, seed = NULL)
tau |
positive integer containing number of observations to simulate |
od |
character containing the name of the conditional distribution of the observations. For details see hsmm |
rd |
character containing the name of the runlength distribution (or sojourn time, dwell time distribution). For details see hsmm |
pi.par |
vector of length J containing the values for the intitial probabilities of the semi-Markov chain |
tpm.par |
matrix of dimension J x J containing the parameter values for the transition probability matrix of the embedded Markov chain. The diagonal entries must all be zero, absorbing states are not permitted |
rd.par |
list with the values for the parameters of the runlength distributions. For details see hsmm |
od.par |
list with the values for the parameters of the conditional observation distributions. For details see hsmm |
M |
positive integer containing the maximum runlength |
seed |
integer. Seed for the random number generator |
The function hsmm.sim
simulates the observations and the underlying state sequence of a
Hidden Semi-Markov Model.
Simulation requires the determination of the runlength and the conditional observation distributions
as well as all parameters.
Note: The simulation of t-distributed conditional observations carries out the function rmt
,
which is extracted from the package csampling
(by Alessandra R. Brazzale).
call |
call |
obs |
|
path |
vector of length the tau containing the simulated underlying semi-Markov chain |
hsmm
, hsmm.smooth
, hsmm.viterbi