get_eval_at

Solves two problems: if setting the number of points too high, the
function becomes slow. If setting it too low, the posterior at large intercept-
changes at change points look discrete, because they are evaluated at very
few x in that interval.

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

get_eval_at function

<dl><dt>fit</dt>
<dd>An mcpfit object.</dd>
<dt>facet_by</dt>
<dd>String. Name of a varying group.</dd></dl>

Arguments

Get a list of x-coordinates to evaluate fit$simulate at — get_eval_at

<dl>

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


<dt>facet_by</dt>
<dd>String. Name of a varying group.</dd>

</dl>

get_eval_at: Get a list of x-coordinates to evaluate fit$simulate at

Description

Usage

Value

Arguments

Details