as_partition_step

<code>as_partition_step()</code> creates a <code>partition_step</code> object. <code>partition_step</code>s
are used while iterating through the partition algorithm: it stores necessary
information about how to proceed in the partitioning, such as the information
threshold. <code>as_partition_step()</code> is primarily called internally by
<code>partition()</code> but can be helpful while developing <code>partitioners</code>.

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

as_partition_step function

<dl><dt>.x</dt>
<dd>a <code>data.frame</code> or <code>partition_step</code> object</dd>
<dt>threshold</dt>
<dd>The minimum information loss allowable</dd>
<dt>reduced_data</dt>
<dd>A data set with reduced variables</dd>
<dt>target</dt>
<dd>A character or integer vector: the variables to reduce</dd>
<dt>metric</dt>
<dd>A measure of information</dd>
<dt>tolerance</dt>
<dd>A tolerance around the threshold to accept a reduction</dd>
<dt>var_prefix</dt>
<dd>Variable name for reduced variables</dd>
<dt>partitioner</dt>
<dd>A <code>partitioner</code>, a <code>part_*()</code> function or one created with
<code>as_partitioner()</code>.</dd>
<dt>...</dt>
<dd>Other objects to store during the partition step</dd></dl>

Arguments

Create a partition object from a data frame — as_partition_step

<dl>

<dt>.x</dt>
<dd>a <code>data.frame</code> or <code>partition_step</code> object</dd>


<dt>threshold</dt>
<dd>The minimum information loss allowable</dd>


<dt>reduced_data</dt>
<dd>A data set with reduced variables</dd>


<dt>target</dt>
<dd>A character or integer vector: the variables to reduce</dd>


<dt>metric</dt>
<dd>A measure of information</dd>


<dt>tolerance</dt>
<dd>A tolerance around the threshold to accept a reduction</dd>


<dt>var_prefix</dt>
<dd>Variable name for reduced variables</dd>


<dt>partitioner</dt>
<dd>A <code>partitioner</code>, a <code>part_*()</code> function or one created with
<code>as_partitioner()</code>.</dd>


<dt>...</dt>
<dd>Other objects to store during the partition step</dd>

</dl>

as_partition_step: Create a partition object from a data frame

Description

Usage

Value

Arguments

Examples