| R_HMMLikelihood {marked} | R Documentation |
Hidden Markov Model Functions
Description
R implementation of HMMs described in processed report except function HMMLikelihood renamed to R_HMMLikelihood and changed to compute values for all capture histories and return lnl, alpha, phi, v, dmat, and gamma values. loglikelihood is called with a fitted hmm model and then computes the gamma,dmat and delta matrices and calls R_HMMLikelihood function. These are not used by the fitting code.
Usage
R_HMMLikelihood(x,first,m,T,dmat,gamma,delta)
loglikelihood(object,ddl=NULL)
Arguments
x |
single observed sequence (capture history) |
first |
occasion to initiate likelihood calculation for sequence |
m |
number of states |
T |
number of occasions; sequence length |
dmat |
observation probability matrices |
gamma |
transition matrices |
delta |
initial distribution |
object |
fitted hmm model |
ddl |
design data list; will be computed if NULL |
Value
both return log-likelihood, alpha, v and phi arrays
Author(s)
Jeff Laake
References
Zucchini, W. and I.L. MacDonald. 2009. Hidden Markov Models for Time Series: An Introduction using R. Chapman and Hall, Boca Raton, FL. 275p. See page 45.