boot.palm

Carries out a parametric bootstrap procedure for models fitted
using the <code>palm</code> package.

Functions to fit point process models using the Palm likelihood. First proposed by Tanaka, Ogata, and Stoyan (2008) <DOI:10.1002/bimj.200610339>, maximisation of the Palm likelihood can provide computationally efficient parameter estimation for point process models in situations where the full likelihood is intractable. This package is chiefly focused on Neyman-Scott point processes, but can also fit the void processes proposed by Jones-Todd et al. (2019) <DOI:10.1002/sim.8046>. The development of this package was motivated by the analysis of capture-recapture surveys on which individuals cannot be identified---the data from which can conceptually be seen as a clustered point process (Stevenson, Borchers, and Fewster, 2019 <DOI:10.1111/biom.12983>). As such, some of the functions in this package are specifically for the estimation of cetacean density from two-camera aerial surveys.

Ben Stevenson

palm

Fitting Point Process Models via the Palm Likelihood

boot.palm function

<dl><dt>fit</dt>
<dd>A fitted object.</dd>
<dt>N</dt>
<dd>The number of bootstrap resamples.</dd>
<dt>prog</dt>
<dd>Logical, if <code>TRUE</code>, a progress bar is printed to
the console.</dd></dl>

Arguments

Bootstrapping for fitted models — boot.palm

<dl>

<dt>fit</dt>
<dd>A fitted object.</dd>


<dt>N</dt>
<dd>The number of bootstrap resamples.</dd>


<dt>prog</dt>
<dd>Logical, if <code>TRUE</code>, a progress bar is printed to
the console.</dd>

</dl>

boot.palm: Bootstrapping for fitted models

Description

Usage

Value

Arguments

Examples