HMMLikelihood: Hidden Markov Model likelihood functions

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

Hidden Markov Model likelihood functions

Description

Function HMMLikelihood computes the log-likelihood via hmm.lnl which is a wrapper for the FORTRAN code hmm_like.f. The function HMMlikelihood is called from optimizer and it in turn calls hmm.lnl after setting up parameters.

For an R version of the HMMLikelihood and related code see R_HMMLikelihood

Usage

HMMLikelihood(par,type,xx,xstart,mx,T,freq=1,fct_dmat,fct_gamma,fct_delta,ddl,
                         dml,parameters,debug=FALSE,return.mat=FALSE,sup=NULL,check=FALSE)
       reals(ddl,dml,parameters,parlist,indices=NULL)
       hmm.lnl(x,start,m,T,dmat,gamma,delta,freq,debug)

Arguments

par	vector of parameter values for log-likelihood evaluation
type	vector of parameter names used to split par vector into types
xx	matrix of observed sequences (row:id; column:occasion/time); xx used instead of x to avoid conflict in optimx
xstart	for each ch, the first non-zero x value and the occasion of the first non-zero value; ; xstart used instead of start to avoid conflict in optimx
mx	number of states; mx used instead of m to avoid conflict in optimx
T	number of occasions; sequence length
freq	vector of history frequencies or 1
fct_dmat	function to create D from parameters
fct_gamma	function to create gamma - transition matrix
fct_delta	function to create initial state distribution
ddl	design data list of parameters for each id
dml	list of design matrices; one entry for each parameter; each entry contains fe and re for fixed and random effects
parameters	formulas for each parameter type
debug	if TRUE, print out par values and -log-likelihood
return.mat	If TRUE, returns list of transition, observation and delta arrays.
sup	list of supplemental information that may be needed by the function but only needs to be computed once; currently only used for MVMS models for dmat
check	if TRUE, checks validity of gamma, dmat and delta to look for any errors
x	same as xx but for call to hmm.lnl
m	same as mx but for call to hmm.lnl
dmat	observation probability matrices
gamma	transition matrices
delta	initial distribution
parlist	list of parameter strings used to split par vector
start	same as xstart but for hmm.lnl
indices	specific indices for computation unless NULL

Value

HMMLikelihood returns log-likelihood for a single sequence and hmm.lnl returns the negative log-likelihood value for each capture history. reals returns either the column dimension of design matrix for parameter or the real parameter vector

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 Also

R_HMMLikelihood

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