<code>icc()</code> efficiently calculates the ICC for a numeric data set.

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

icc function

<dl><dt>.x</dt>
<dd>a data set</dd>
<dt>method</dt>
<dd>The method source: both the pure R and C++ versions are efficient</dd></dl>

Arguments

Calculate the intraclass correlation coefficient — icc

<dl>

<dt>.x</dt>
<dd>a data set</dd>


<dt>method</dt>
<dd>The method source: both the pure R and C++ versions are efficient</dd>

</dl>

icc: Calculate the intraclass correlation coefficient

Description

Usage

Value

Arguments

Examples