nLogLike_rcpp: Negative log-likelihood

In momentuHMM: Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

Negative log-likelihood

Description

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

Usage

nLogLike_rcpp(
  nbStates,
  covs,
  data,
  dataNames,
  dist,
  Par,
  aInd,
  zeroInflation,
  oneInflation,
  stationary,
  knownStates,
  betaRef,
  mixtures
)

Arguments

nbStates	Number of states,
covs	Covariates,
data	A momentuHMMData object of the observations,
dataNames	Character vector containing the names of the data streams,
dist	Named list indicating the probability distributions of the data streams.
Par	Named list containing the state-dependent parameters of the data streams, matrix of regression coefficients for the transition probabilities ('beta'), and initial distribution ('delta').
aInd	Vector of indices of the rows at which the data switches to another animal
zeroInflation	Named list of logicals indicating whether the probability distributions of the data streams are zero-inflated.
oneInflation	Named list of logicals indicating whether the probability distributions of the data streams are one-inflated.
stationary	false if there are time-varying covariates in formula or any covariates in formulaDelta. If true, the initial distribution is considered equal to the stationary distribution. Default: false.
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.
betaRef	Indices of reference elements for t.p.m. multinomial logit link.
mixtures	Number of mixtures for the state transition probabilities

Value

Negative log-likelihood

momentuHMM documentation built on Oct. 19, 2022, 1:07 a.m.