prob.deriv

Used in call to DeltaMethod from prob.se to get first derivatives

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

prob.deriv function

<dl><dt>par</dt>
<dd>detection function parameter values</dd>
<dt>model</dt>
<dd>ddf model object</dd>
<dt>parfct</dt>
<dd>function of detection probabilities; currently only
average (over covariates) detection probability p integrated over distance
or average (over covariates) detection probability at distance 0; p(0)</dd>
<dt>observer</dt>
<dd>1,2,3 for primary, secondary, or duplicates for average
p(0); passed to fct</dd>
<dt>fittedmodel</dt>
<dd>full fitted ddf model when <code>trial.fi</code> or
<code>io.fi</code> is called from <code>trial</code> or <code>io</code> respectively</dd></dl>

Arguments

Author

Derivatives for variance of average p and average p(0) variance — prob.deriv

<dl>

<dt>par</dt>
<dd>detection function parameter values</dd>


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


<dt>parfct</dt>
<dd>function of detection probabilities; currently only
average (over covariates) detection probability p integrated over distance
or average (over covariates) detection probability at distance 0; p(0)</dd>


<dt>observer</dt>
<dd>1,2,3 for primary, secondary, or duplicates for average
p(0); passed to fct</dd>


<dt>fittedmodel</dt>
<dd>full fitted ddf model when <code>trial.fi</code> or
<code>io.fi</code> is called from <code>trial</code> or <code>io</code> respectively</dd>

</dl>

prob.deriv: Derivatives for variance of average p and average p(0) variance

Description

Usage

Value

Arguments

Author

Details

See Also