test_set

Simulate time series with known changepoint sets

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

test_set function

<dl><dt>n</dt>
<dd>Number of true changepoints in set</dd>
<dt>sd</dt>
<dd>Standard deviation passed to <code><a href="/link/stats%3A%3Arnorm()?package=tidychangepoint&version=1.0.0" data-mini-rdoc="tidychangepoint::stats::rnorm()">stats::rnorm()</a></code></dd>
<dt>seed</dt>
<dd>Value passed to <code><a href="/link/base%3A%3Aset.seed()?package=tidychangepoint&version=1.0.0" data-mini-rdoc="tidychangepoint::base::set.seed()">base::set.seed()</a></code></dd></dl>

Arguments

Simulate time series with known changepoint sets — test_set

<dl>

<dt>n</dt>
<dd>Number of true changepoints in set</dd>


<dt>sd</dt>
<dd>Standard deviation passed to <code><a href='https://rdrr.io/r/stats/Normal.html'>stats::rnorm()</a></code></dd>


<dt>seed</dt>
<dd>Value passed to <code><a href='https://rdrr.io/r/base/Random.html'>base::set.seed()</a></code></dd>

</dl>

test_set: Simulate time series with known changepoint sets

Description

Usage

Value

Arguments

See Also

Examples