plot.clusEvol

Plot density or empirical cumulative distribution from <code>Bvals</code> in <code>clusEvol output</code>.

clusEvol

Cluster Evolution Analytics allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2024) <https://mpra.ub.uni-muenchen.de/120220>.

V<c3><ad>ctor Morales-O<c3><b1>ate

A Procedure for Cluster Evolution Analytics

Víctor Morales-Oñate

Bolívar Morales-Oñate

plot.clusEvol function

<dl>
 <dt>x</dt>
<dd>an object of the class <code>"clusEvol"</code></dd> <dt>target</dt>
<dd>Numeric; 1 if density, 2 if ecdf plot is returned</dd> <dt>type</dt>
<dd>Character; <code>heat</code> (default), <code>line</code>, <code>boxplot</code> are the options</dd> <dt>plotly</dt>
<dd>Logical; if <code>FALSE</code>, a <code>ggplotly</code> plot is returned</dd> <dt>...</dt>
<dd>other arguments to be passed to the function
 <code>ggplot</code></dd></dl>

Arguments

Plot output results from clusEvol — plot.clusEvol

<dl>


 <dt>x</dt>
<dd>an object of the class <code>"clusEvol"</code></dd>

 <dt>target</dt>
<dd>Numeric; 1 if density, 2 if ecdf plot is returned</dd>

 <dt>type</dt>
<dd>Character; <code>heat</code> (default), <code>line</code>, <code>boxplot</code> are the options</dd>

 <dt>plotly</dt>
<dd>Logical; if <code>FALSE</code>, a <code>ggplotly</code> plot is returned</dd>

 <dt>...</dt>
<dd>other arguments to be passed to the function
 <code>ggplot</code></dd>

</dl>

plot.clusEvol: Plot output results from clusEvol

Description

Usage

Value

Arguments

Details

See Also