R_HMMLikelihood: Hidden Markov Model Functions

In marked: Mark-Recapture Analysis for Survival and Abundance Estimation

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.

marked documentation built on Oct. 19, 2023, 5:06 p.m.