get_summary_stats

An internal function that calculates the sufficient statistics to be used
within the reversible-jump MCMC Gibbs sampler called by
<code>link{samp_move}</code>.

Methods for assessing animal movement from telemetry and biologging
data using non-parametric Bayesian methods. This includes features for pre-
processing and analysis of data, as well as the visualization of results
from the models. This framework does not rely on standard parametric density
functions, which provides flexibility during model fitting. Further details
regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.

Joshua Cullen

bayesmove

Non-Parametric Bayesian Analyses of Animal Movement

Denis Valle

get_summary_stats function

<dl><dt>breakpt</dt>
<dd>numeric. A vector of breakpoints.</dd>
<dt>dat</dt>
<dd>A matrix that only contains columns storing discretized data for
each of the movement variables.</dd>
<dt>max.time</dt>
<dd>numeric. The number of of the last observation of <code>dat</code>.</dd>
<dt>nbins</dt>
<dd>numeric. A vector of the number of bins used to discretize each
movement variable. These must be in the same order as the columns within
<code>dat</code>.</dd>
<dt>ndata.types</dt>
<dd>numeric. The length of <code>nbins</code>.</dd></dl>

Arguments

Internal function that calculates the sufficient statistics for the
segmentation model — get_summary_stats

<dl>

<dt>breakpt</dt>
<dd>numeric. A vector of breakpoints.</dd>


<dt>dat</dt>
<dd>A matrix that only contains columns storing discretized data for
each of the movement variables.</dd>


<dt>max.time</dt>
<dd>numeric. The number of of the last observation of <code>dat</code>.</dd>


<dt>nbins</dt>
<dd>numeric. A vector of the number of bins used to discretize each
movement variable. These must be in the same order as the columns within
<code>dat</code>.</dd>


<dt>ndata.types</dt>
<dd>numeric. The length of <code>nbins</code>.</dd>

</dl>

get_summary_stats: Internal function that calculates the sufficient statistics for the segmentation model

Description

Usage

Value

Arguments