check.covar.uncertainty

<p>dataframe containing information used to 
parametrically resample data or NULL if not required</p>

covariate.uncertainty

<p>Ensures that where necessary the values are characters and that only 
supported sampling distributions have been selected.</p>

internal

Performs distance sampling analyses on a number of species
at once and can account for unidentified sightings, model uncertainty
and covariate uncertainty. Unidentified sightings refer to sightings
which cannot be allocated to a single species but may instead be
allocated to a group of species. The abundance of each unidentified
group is estimated and then prorated to the species estimates. Model
uncertainty should be incorporated when multiple models give equally
good fit to the data but lead to large differences in estimated
density / abundance. Covariate uncertainty should be incorporated
when covariates cannot be measured accurately, for example this is
often the case for group size in marine mammal surveys. Variance
estimation for these methods is via a non parametric bootstrap.
The methods implemented are described in Gerodette T. and Forcada
J. (2005) <doi:10.3354/meps291001> Non-recovery of two spotted and
spinner dolphin populations in the eastern tropical Pacific Ocean.

check.covar.uncertainty: Performs checks on the covariate.uncertainty dataframe

Description

Usage

Arguments

Value

See Also