log_lambda_log_likelihood

Log of poisson intensities for total areas sampled in each sampled strata

Log.lambda

linear predictor parameters for the log of abundance intensity

Beta

a vector of spatial random effects

standard deviation of the overdispersion process

a vector giving the current iteration's number of groups in the area

Index for which cells were actually sampled

Sampled

Total area sampled in each sampled cell

Area

compute the likelihood for nu parameters

Functions for performing hierarchical analysis of distance
sampling data, with ability to use an areal spatial ICAR model on
top of user supplied covariates to get at variation in abundance
intensity.  The detection model can be specified as a function of
observer and individual covariates, where a parametric model is
supposed for the population level distribution of covariate values.
The model uses data augmentation and a reversible jump MCMC
algorithm to sample animals that were never observed.  Also
included is the ability to include point independence (increasing
correlation multiple observer's observations as a function of
distance, with independence assumed for distance=0 or first
distance bin), as well as the ability to model species
misclassification rates using a multinomial logit formulation on data
from double observers.  There is also the the ability to
include zero inflation, but this is only recommended for cases where
sample sizes and spatial coverage of the survey are high.

log_lambda_log_likelihood: compute the likelihood for nu parameters

Description

Usage

Arguments

Value