stack_data_misID

Data

matrix giving the total numer of groups observed at least once by species and transect

G.obs

total number of observations for animals seen at least once

g.tot.obs

vector giving number of observers per transect

n.Observers

n.transects

n.species

column names for new stacked dataset

stacked.names

a vector of indicator variables (1 = factor/categorical variable, 0 = continuous variable)

factor.ind

function to stack data for midID updates (going from four dimensional array to a two dimensional array including observed groups

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.

stack_data_misID: function to stack data for midID updates (going from four dimensional array to a two dimensional array including observed groups

Description

Usage

Arguments

Value