ex_binomial

See how this data was simulated
<a href="https://github.com/lindeloev/mcp/blob/master/data-raw/ex_binomial.R">here</a>
using <code>fit$simulate()</code>, including which parameters were used. See an analysis
<a href="https://lindeloev.github.io/mcp/">here</a>.

datasets

Flexible and informed regression with Multiple Change Points (MCP). 'mcp' can infer change points in means, variances, autocorrelation structure, and any combination of these, as well as the parameters of the segments in between. All parameters are estimated with uncertainty and prediction intervals are supported - also near the change points. 'mcp' supports hypothesis testing via Savage-Dickey density ratio, posterior contrasts, and cross-validation. 'mcp' is described in Lindel<c3><b8>v (submitted) <doi:10.31219/osf.io/fzqxv> and generalizes the approach described in Carlin, Gelfand, & Smith (1992) <doi:10.2307/2347570> and Stephens (1994) <doi:10.2307/2986119>.

Jonas Kristoffer Lindel<c3><b8>v

Regression with Multiple Change Points

ex_binomial function

A data frame with 100 rows and 3 variables:<dl class="dl-horizontal">
<dt>x</dt><dd>The x-axis.</dd>
<dt>y</dt><dd>The y-axis.</dd>
<dt>N</dt><dd>The number of trials for for <code>y</code>.</dd>
</dl>

Format

See how this data was simulated
<a href='https://github.com/lindeloev/mcp/blob/master/data-raw/ex_binomial.R'>here</a>
using <code>fit$simulate()</code>, including which parameters were used. See an analysis
<a href='https://lindeloev.github.io/mcp/'>here</a>.

Two change points between three binomial segments — ex_binomial

A data frame with 100 rows and 3 variables:<dl class='dl-horizontal'>
<dt>x</dt><dd>The x-axis.</dd>
<dt>y</dt><dd>The y-axis.</dd>
<dt>N</dt><dd>The number of trials for for <code>y</code>.</dd>
</dl>

ex_binomial: Two change points between three binomial segments

Description

Usage

Arguments

Format