| HiddenMarkov-mmpp-deprecated {HiddenMarkov} | R Documentation |
These functions are deprecated and will ultimately be removed from the package. Please change to the revised versions: BaumWelch, Estep.mmpp, forwardback.mmpp, simulate or logLik.
backward0.mmpp(tau, Q, lambda)
forward0.mmpp(tau, Q, delta, lambda)
logLikmmpp(tau, Q, delta, lambda)
Estep0.mmpp(tau, Q, delta, lambda)
Baum.Welch.mmpp(tau, Q, delta, lambda, nonstat = TRUE,
maxiter = 500, tol = 1e-05, prt = TRUE,
converge = expression(diff < tol))
Baum.Welch0.mmpp(tau, Q, delta, lambda, nonstat = TRUE,
maxiter = 500, tol = 1e-05, prt = TRUE,
converge = expression(diff < tol))
sim.mmpp(n, initial, Q, lambda)
tau |
vector containing the interevent times. Note that the first event is at time zero. |
Q |
the infinitesimal generator matrix of the Markov process. |
lambda |
a vector containing the Poisson rates. |
delta |
is the marginal probability distribution of the m hidden states at time zero. |
n |
number of Poisson events to be simulated. |
initial |
integer, being the initial hidden Markov state (1, cdots, m). |
nonstat |
is logical, TRUE if the homogeneous Markov chain is assumed to be non-stationary, default. |
maxiter |
is the maximum number of iterations, default is 500. |
tol |
is the convergence criterion, being the difference between successive values of the log-likelihood; default is 0.00001. |
prt |
is logical, and determines whether information is printed at each iteration; default is TRUE. |
converge |
is an expression giving the convergence criterion. |
The functions with a suffix of zero are non-scaled, and hence will have numerical problems for series containing larger numbers of events; and are much slower.
These functions use the algorithm given by Ryden (1996) based on eigenvalue decompositions.