assign_partition

the data or a <code>partition_step</code> object

a <code>partitioner</code>. See the <code>part_*()</code> functions and
<code><a rd-options="=as_partitioner" href="/link/as_partitioner()?package=partition&version=0.1.0&to=%3Das_partitioner" data-mini-rdoc="=as_partitioner::as_partitioner()">as_partitioner()</a></code>.

partitioner

.data

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.

threshold

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

tolerance

<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.

Malcolm Barrett

partition

Agglomerative Partitioning Framework for Dimension Reduction

Joshua Millstein

assign_partition function

a <code>partitioner</code>. See the <code>part_*()</code> functions and
<code><a rd-options='=as_partitioner' href='as_partitioner()'>as_partitioner()</a></code>.

assign_partition: Process a dataset with a partitioner

Description

Usage

Arguments

Value