summary.io.fi

Provides a brief summary of data and fitted detection probability model
parameters, model selection criterion, and optionally abundance in the
covered (sampled) region and its standard error.

utility

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

summary.io.fi function

<dl><dt>object</dt>
<dd>a <code>ddf</code> model object</dd>
<dt>se</dt>
<dd>if TRUE, computes standard errors</dd>
<dt>N</dt>
<dd>if TRUE, computes abundance in covered (sampled) region</dd>
<dt>fittedmodel</dt>
<dd>full fitted model when called from <code>trial</code> or
<code>io</code></dd>
<dt>ddfobj</dt>
<dd>distance sampling object description</dd>
<dt>...</dt>
<dd>unspecified and unused arguments for S3 consistency</dd></dl>

Arguments

Author

Summary of distance detection function model object — summary.io.fi

<dl>

<dt>object</dt>
<dd>a <code>ddf</code> model object</dd>


<dt>se</dt>
<dd>if TRUE, computes standard errors</dd>


<dt>N</dt>
<dd>if TRUE, computes abundance in covered (sampled) region</dd>


<dt>fittedmodel</dt>
<dd>full fitted model when called from <code>trial</code> or
<code>io</code></dd>


<dt>ddfobj</dt>
<dd>distance sampling object description</dd>


<dt>...</dt>
<dd>unspecified and unused arguments for S3 consistency</dd>

</dl>

summary.io.fi: Summary of distance detection function model object

Description

Usage

Value

Arguments

Author

Details