model_variance

Changepoint detection algorithms for R are widespread but have
different interfaces and reporting conventions.
This makes the comparative analysis of results difficult.
We solve this problem by providing a tidy, unified interface for several
different changepoint detection algorithms.
We also provide consistent numerical and graphical reporting leveraging
the 'broom' and 'ggplot2' packages.

Ben Baumer

tidychangepoint

A Tidy Framework for Changepoint Detection Analysis

Benjamin S. Baumer

Biviana Marcela Suárez Sierra

Arrigo Coen

Carlos A. Taimal

Xueheng Shi

model_variance function

<dl><dt>object</dt>
<dd>A model object implementing <code><a href="/link/residuals()?package=tidychangepoint&version=1.0.0" data-mini-rdoc="tidychangepoint::residuals()">residuals()</a></code> and <code><a href="/link/nobs()?package=tidychangepoint&version=1.0.0" data-mini-rdoc="tidychangepoint::nobs()">nobs()</a></code></dd>
<dt>...</dt>
<dd>currently ignored</dd></dl>

Arguments

Compute model variance — model_variance

<dl>

<dt>object</dt>
<dd>A model object implementing <code><a href='https://rdrr.io/r/stats/residuals.html'>residuals()</a></code> and <code><a href='https://rdrr.io/r/stats/nobs.html'>nobs()</a></code></dd>


<dt>...</dt>
<dd>currently ignored</dd>

</dl>

Compute model variance

model_variance: Compute model variance

Description

Usage

Value

Arguments

Details