count_clusters

total_reduced

summarize_mapping

minimum_information

Helper functions to print <code>partition</code> summary

internal

A fast and flexible framework for agglomerative partitioning.
'partition' uses an approach called Direct-Measure-Reduce to create
new variables that maintain the user-specified minimum level of
information. Each reduced variable is also interpretable: the original
variables map to one and only one variable in the reduced data set.
'partition' is flexible, as well: how variables are selected to
reduce, how information loss is measured, and the way data is reduced
can all be customized. 'partition' is based on the Partition
framework discussed in Millstein et al. (2020)
<doi:10.1093/bioinformatics/btz661>.

Malcolm Barrett

partition

Agglomerative Partitioning Framework for Dimension Reduction

Joshua Millstein

Katelyn Queen

count_clusters function

<dl><dt>.partition</dt>
<dd>a <code>partition</code> object</dd>
<dt>n_composite</dt>
<dd>number of composite variables to print before summarizing</dd>
<dt>n_reduced</dt>
<dd>number of reduced variables to print before summarizing</dd>
<dt>.round</dt>
<dd>Should the minimum information be rounded?</dd>
<dt>digits</dt>
<dd>If <code>.round</code> is <code>TRUE</code>, to what digit should it be rounded?</dd></dl>

Arguments

Helper functions to print partition summary — count_clusters

<dl>

<dt>.partition</dt>
<dd>a <code>partition</code> object</dd>


<dt>n_composite</dt>
<dd>number of composite variables to print before summarizing</dd>


<dt>n_reduced</dt>
<dd>number of reduced variables to print before summarizing</dd>


<dt>.round</dt>
<dd>Should the minimum information be rounded?</dd>


<dt>digits</dt>
<dd>If <code>.round</code> is <code>TRUE</code>, to what digit should it be rounded?</dd>

</dl>

count_clusters: Helper functions to print `partition` summary

Description

Usage

Arguments