measure_variance_explained

Metrics are functions that tell how much information would be
lost for a given reduction in the data. reduce. <code>as_measure()</code> is a
helper function to create new metrics to be used in <code>partitioner</code>s.
<code>partitioner</code>s can be created with <code>as_partitioner()</code>.
<code>measure_variance_explained()</code> assesses information loss by
calculating the variance explained by the first component of a principal
components analysis. Because the PCA calculates the components and the
variance explained at the same time, if the reducer is
<code>reduce_first_component()</code>, then <code>measure_variance_explained()</code> will store
the first component for later use to avoid recalculation.

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

measure_variance_explained function

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

Arguments

Measure the information loss of reduction using the variance explained. — measure_variance_explained

<dl>

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

</dl>

measure_variance_explained: Measure the information loss of reduction using the variance explained.

Description

Usage

Value

Arguments

See Also