fit.ddf.models

list of dataframes containing the data to which the 
models will be fitted

ddf.dat.working

list of unique character vectors giving the names of the 
ddf objects for each species.

model.names

ddf.models

character option specifying the model selection criteria - 
"AIC", "AICc" or "BIC".

criterion

bootstrap.ddf.statistics

numeric value indicating iteration number

rep.no

character vector of warning messages

MAE.warnings

Fits all the models named in model.names to the associated data supplied in 
ddf.dat.working. If more than one model is supplied for any species the 
model with the minimum selection criteria will be selected.

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.

fit.ddf.models: Refits the detection functions to the resampled data

Description

Usage

Arguments

Value