assign_partition

<code>assign_partition()</code> is the primary handler for the partition algorithm and
is iterated by <code>reduce_partition_c()</code>. <code>assign_partition()</code> does initial set
up of the <code>partition_step</code> object and then applies the <code>partitioner</code> to each
iteration of the <code>partition_step</code> via <code>direct_measure_reduce()</code>.

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

assign_partition function

<dl><dt>.x</dt>
<dd>the data or a <code>partition_step</code> object</dd>
<dt>partitioner</dt>
<dd>a <code>partitioner</code>. See the <code>part_*()</code> functions and
<code>as_partitioner()</code>.</dd>
<dt>.data</dt>
<dd>a data.frame to partition</dd>
<dt>threshold</dt>
<dd>the minimum proportion of information explained by a reduced
variable; <code>threshold</code> sets a boundary for information loss because each
reduced variable must explain at least as much as <code>threshold</code> as measured
by the metric.</dd>
<dt>tolerance</dt>
<dd>a small tolerance within the threshold; if a reduction is
within the threshold plus/minus the tolerance, it will reduce.</dd></dl>

Arguments

Process a dataset with a partitioner — assign_partition

<dl>

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


<dt>partitioner</dt>
<dd>a <code>partitioner</code>. See the <code>part_*()</code> functions and
<code>as_partitioner()</code>.</dd>


<dt>.data</dt>
<dd>a data.frame to partition</dd>


<dt>threshold</dt>
<dd>the minimum proportion of information explained by a reduced
variable; <code>threshold</code> sets a boundary for information loss because each
reduced variable must explain at least as much as <code>threshold</code> as measured
by the metric.</dd>


<dt>tolerance</dt>
<dd>a small tolerance within the threshold; if a reduction is
within the threshold plus/minus the tolerance, it will reduce.</dd>

</dl>

assign_partition: Process a dataset with a partitioner

Description

Usage

Value

Arguments