mcmclist_samples

mcmclist_samples.mcpfit

Returns posterior samples, if available. If not, then prior samples. If not,
then throw an informative error. This is useful for summary and plotting, that
works on both.

internal

Flexible and informed regression with Multiple Change Points. '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 Lindeløv (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

Jonas Kristoffer Lindeløv

mcmclist_samples function

<dl><dt>fit</dt>
<dd>An <code>mcpfit</code> object</dd>
<dt>prior</dt>
<dd>TRUE/FALSE. Summarise prior instead of posterior?</dd>
<dt>message</dt>
<dd>TRUE: gives a message if returning prior samples. FALSE = no message</dd>
<dt>error</dt>
<dd>TRUE: err if there are no samples. FALSE: return NULL</dd></dl>

Arguments

Internal function to get samples. — mcmclist_samples

<dl>

<dt>fit</dt>
<dd>An <code>mcpfit</code> object</dd>


<dt>prior</dt>
<dd>TRUE/FALSE. Summarise prior instead of posterior?</dd>


<dt>message</dt>
<dd>TRUE: gives a message if returning prior samples. FALSE = no message</dd>


<dt>error</dt>
<dd>TRUE: err if there are no samples. FALSE: return NULL</dd>

</dl>

mcmclist_samples: Internal function to get samples.

Description

Usage

Arguments