recover_levels

Jags uses 1, 2, 3, ..., etc. for indexing of varying effects.
This function adds back the original levels, whether numeric or string

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

recover_levels function

<dl><dt>samples</dt>
<dd>An mcmc.list with varying columns starting in <code>mcmc_col</code>.</dd>
<dt>data</dt>
<dd>A tibble or data.frame with the cols in <code>data_col</code>.</dd>
<dt>mcmc_col</dt>
<dd>A vector of strings.</dd>
<dt>data_col</dt>
<dd>A vector of strings. Has to be same length as <code>mcmc_col</code>.`</dd></dl>

Arguments

Recover the levels of varying effects in mcmc.list — recover_levels

<dl>

<dt>samples</dt>
<dd>An mcmc.list with varying columns starting in <code>mcmc_col</code>.</dd>


<dt>data</dt>
<dd>A tibble or data.frame with the cols in <code>data_col</code>.</dd>


<dt>mcmc_col</dt>
<dd>A vector of strings.</dd>


<dt>data_col</dt>
<dd>A vector of strings. Has to be same length as <code>mcmc_col</code>.`</dd>

</dl>

recover_levels: Recover the levels of varying effects in mcmc.list

Description

Usage

Arguments