HiddenMarkov-mmpp-deprecated: Markov Modulated Poisson Process - Deprecated Functions
In HiddenMarkov: Hidden Markov Models

Description Usage Arguments Details

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.

HiddenMarkov documentation built on April 27, 2021, 5:06 p.m.