nLogLike_rcpp: Negative log-likelihood
In moveHMM: Animal Movement Modelling using Hidden Markov Models

nLogLike_rcpp

R Documentation

Negative log-likelihood

Description

Computation of the negative log-likelihood (forward algorithm - written in C++)

Usage

nLogLike_rcpp(
  nbStates,
  beta,
  covs,
  data,
  stepDist,
  angleDist,
  stepPar,
  anglePar,
  delta,
  aInd,
  zeroInflation,
  stationary,
  knownStates
)

Arguments

`nbStates`	Number of states
`beta`	Matrix of regression coefficients for the transition probabilities
`covs`	Covariates
`data`	A `moveData` object of the observations
`stepDist`	The name of the step length distribution
`angleDist`	The name of the turning angle distribution
`stepPar`	State-dependent parameters of the step length distribution
`anglePar`	State-dependent parameters of the turning angle distribution
`delta`	Stationary distribution
`aInd`	Vector of indices of the rows at which the data switches to another animal
`zeroInflation`	`true` if zero-inflation is included in the step length distribution, `false` otherwise.
`stationary`	`false` if there are covariates. If `true`, the initial distribution is considered equal to the stationary distribution.
`knownStates`	Vector of values of the state process which are known prior to fitting the model (if any). Default: NULL (states are not known). This should be a vector with length the number of rows of 'data'; each element should either be an integer (the value of the known states) or NA if the state is not known.