unpack_y

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

unpack_y function

<dl><dt>form_y</dt>
<dd>Character representation of formula</dd>
<dt>i</dt>
<dd>The segment number (integer)</dd>
<dt>family</dt>
<dd>An mcpfamily object returned by <code>mcpfamily()</code>.</dd></dl>

Arguments

Jonas Kristoffer Lindeløv <a href="/link/jonas%40lindeloev.dk?package=mcp&version=0.3.4" data-mini-rdoc="mcp::jonas@lindeloev.dk">jonas@lindeloev.dk</a>

Author

Unpacks y variable name — unpack_y

<dl>

<dt>form_y</dt>
<dd>Character representation of formula</dd>


<dt>i</dt>
<dd>The segment number (integer)</dd>


<dt>family</dt>
<dd>An mcpfamily object returned by <code>mcpfamily()</code>.</dd>

</dl>

Jonas Kristoffer Lindeløv <a href='mailto:jonas@lindeloev.dk'>jonas@lindeloev.dk</a>

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unpack_y: Unpacks y variable name

Description

Usage

Value

Arguments

Author