Mark-Recapture Distance Sampling (MRDS) Analysis of Trial Observer Configuration and Point Independence
1 2 3
distance sampling model specification; model list with key function and scale formula if any
mark-recapture model specfication; model list with formula and link
list containing settings controlling data structure
list containing settings controlling model fitting
original function call used to call
MRDS analysis based on point independence involves two separate and
independent analyses of the mark-recapture data and the distance sampling
data. For the trial configuration, the mark-recapture data are analysed
with a call to
ddf.trial.fi (see likelihood eq 6.12 and 6.17
in Laake and Borchers 2004) to fit a conditional distance sampling detection
function for observer 1 based on trials (observations) from observer 2 to
estimate p_1(0), detection probability at distance zero for observer 1.
Independently, the distance data from observer 1 are used to fit a
conventional distance sampling (CDS) (likelihood eq 6.6) or multi-covariate
distance sampling (MCDS) (likelihood eq 6.14) model for the detection
function, g(y), such that g(0)=1. The detection function for observer 1 is
then created as p_1(y)=p_1(0)*g(y) (eq 6.28 of Laake and Borchers 2004) from
which predictions are made.
ddf.trial is not called directly by the
user and is called from
For a complete description of each of the calling arguments, see
ddf. The argument
dataname is the name of the
dataframe specified by the argument
ddf. The arguments
defined the same as in
result: a trial model object which is composed of
ds model objects
Laake, J.L. and D.L. Borchers. 2004. Methods for incomplete detection at distance zero. In: Advanced Distance Sampling, eds. S.T. Buckland, D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. Oxford University Press.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.