tidy_samples

tidy_samples.mcpfit

An <code><a rd-options="" href="/link/mcpfit?package=mcp&version=0.3.0" data-mini-rdoc="mcp::mcpfit">mcpfit</a></code> object

<ul>
<li>TRUE: All population effects. Same as <code>fit$pars$population</code>.<ul>
<li>FALSE: No population effects. Same as <code>c()</code>.</li>
<li>Character vector: Only include specified population parameters - see <code>fit$pars$population</code>.</li>
</ul></li>
</ul>

population

<ul>
<li>TRUE: All varying effects (<code>fit$pars$varying</code>).<ul>
<li>FALSE: No varying efects (<code>c()</code>).</li>
<li>Character vector: Only include specified varying parameters - see <code>fit$pars$varying</code>.</li>
</ul></li>
</ul>

varying

<ul>
<li>TRUE: Returns the absolute location of all varying change points.<ul>
<li>FALSE: Just returns the varying effects.</li>
<li>Character vector: Only do absolute transform for these varying parameters - see <code>fit$pars$varying</code>.</li>
</ul>OBS: This currently only applies to varying change points. It is not implemented for <code>rel()</code> regressors yet.</li>
</ul>

absolute

TRUE/FALSE. Summarise prior instead of posterior?

prior

Integer or <code>NULL</code>. Number of samples to return/summarise.
If there are varying effects, this is the number of samples from each varying group.
<code>NULL</code> means "all". Ignored if both are <code>FALSE</code>. More samples trade speed for accuracy.

nsamples

Returns in a format useful for <code>fit$simulate()</code> with population parameters in wide format
and varying effects in long format (the number of rows will be <code>nsamples * n_levels_in_varying</code>).

internal

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

tidy_samples function

An <code><a rd-options='' href='mcpfit'>mcpfit</a></code> object

<ul>
<li>TRUE: Returns the absolute location of all varying change points.<ul>
<li>FALSE: Just returns the varying effects.</li>
<li>Character vector: Only do absolute transform for these varying parameters - see <code>fit$pars$varying</code>.</li>
</ul>

OBS: This currently only applies to varying change points. It is not implemented for <code>rel()</code> regressors yet.</li>
</ul>

tidy_samples: Get tidy samples with or without varying effects

Description

Usage

Arguments

Value