fill_in_missing

swap_nans

Process reduced variables when missing data

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

fill_in_missing function

<dl><dt>x</dt>
<dd>a vector, the reduced variable</dd>
<dt>.na</dt>
<dd>a logical vector marking which are missing</dd>
<dt>.fill</dt>
<dd>what to fill the missing locations with</dd></dl>

Arguments

Process reduced variables when missing data — fill_in_missing

<dl>

<dt>x</dt>
<dd>a vector, the reduced variable</dd>


<dt>.na</dt>
<dd>a logical vector marking which are missing</dd>


<dt>.fill</dt>
<dd>what to fill the missing locations with</dd>

</dl>

fill_in_missing: Process reduced variables when missing data

Description

Usage

Value

Arguments