reduce_first_component

Reducers are functions that tell the partition algorithm how
to reduce the data. <code>as_reducer()</code> is a helper function to create new
reducers to be used in <code>partitioner</code>s. <code>partitioner</code>s can be created with
<code>as_partitioner()</code>.
<code>reduce_first_component()</code> returns the first component from the
principal components analysis of the target variables.

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

reduce_first_component function

<dl><dt>.partition_step</dt>
<dd>a <code>partition_step</code> object</dd></dl>

Arguments

Reduce selected variables to first principal component — reduce_first_component

<dl>

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

</dl>

reduce_first_component: Reduce selected variables to first principal component

Description

Usage

Value

Arguments

See Also