gmEM {ctmcd}

Expectation-Maximization Algorithm

Description

Function for deriving a Markov generator matrix estimate by an instance of the expectation-maximization algorithm (described by Bladt and Soerensen, 2005)

Usage

gmEM(tmabs, te, gmguess, eps = 1e-06, niter = 10000, expmethod = "PadeRBS", 
verbose = FALSE)

Arguments

tmabs

matrix of absolute transition frequencies

te

time elapsed in transition process

gmguess

initial guess (for generator matrix)

eps

stop criterion: stop, if relative change in log-likelihood is smaller than eps

niter

stop criterion: maximum number of iterations

expmethod

method for computation of matrix exponential, by default "PadeRBS" is chosen (see ?expm from expm package for more information)

verbose

verbose mode

Details

A maximum likelihood generator matrix estimate is derived by an instance of the expectation-maximization algorithm.

Value

generator matrix estimate

Author(s)

Marius Pfeuffer

References

M. Bladt and M. Soerensen: Statistical Inference for Discretely Observed Markov Jump Processes. Journal of the Royal Statistical Society B 67(3):395-410, 2005

Examples

data(tm_abs)

## Initial guess for generator matrix (absorbing default state)
gm0=matrix(1,8,8)
diag(gm0)=0
diag(gm0)=-rowSums(gm0)
gm0[8,]=0

## Derive expectation-maximization algorithm generator matrix estimate
gmem=gmEM(tmabs=tm_abs,1,gmguess=gm0,verbose=TRUE)
gmem