logisticdupbyx_fast

As <code>logisticdupbyx</code>, but faster when distance is a covariate
(but no interactions with distance occur.

Animal abundance estimation via conventional, multiple covariate
and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function
fitting is performed via maximum likelihood. Also included are diagnostics
and plotting for fitted detection functions. Abundance estimation is via a
Horvitz-Thompson-like estimator.

Laura Marshall

mrds

Mark-Recapture Distance Sampling

Jeff Laake

David Miller

Felix Petersma

Len Thomas

David Borchers

Jon Bishop

Jonah McArthur

Eric Rexstad

logisticdupbyx_fast function

<dl><dt>distance</dt>
<dd>vector of distance values</dd>
<dt>x1</dt>
<dd>linear predictor for 1, without distance</dd>
<dt>x2</dt>
<dd>linear predictor for 2, without distance</dd>
<dt>models</dt>
<dd>model list</dd>
<dt>beta</dt>
<dd>logistic parameters</dd>
<dt>point</dt>
<dd><code>TRUE</code> for point transect data</dd>
<dt>beta_distance</dt>
<dd>parameter for distance</dd></dl>

Arguments

Author

Logistic for duplicates as a function of covariates (fast) — logisticdupbyx_fast

<dl>

<dt>distance</dt>
<dd>vector of distance values</dd>


<dt>x1</dt>
<dd>linear predictor for 1, without distance</dd>


<dt>x2</dt>
<dd>linear predictor for 2, without distance</dd>


<dt>models</dt>
<dd>model list</dd>


<dt>beta</dt>
<dd>logistic parameters</dd>


<dt>point</dt>
<dd><code>TRUE</code> for point transect data</dd>


<dt>beta_distance</dt>
<dd>parameter for distance</dd>

</dl>

Logistic for duplicates as a function of covariates (fast)

logisticdupbyx_fast: Logistic for duplicates as a function of covariates (fast)

Description

Usage

Arguments

Author