income

<p>Like the mock data frame "abcd_h_income", but with "unique_id" as the "uid".</p>
<p>Like the mock data frame "abcd_cort_sa", but with "unique_id" as the "uid".</p>

datasets

Framework to facilitate patient subtyping with similarity network fusion and meta clustering. The similarity network fusion (SNF) algorithm was introduced by Wang et al. (2014) in <doi:10.1038/nmeth.2810>. SNF is a data integration approach that can transform high-dimensional and diverse data types into a single similarity network suitable for clustering with minimal loss of information from each initial data source. The meta clustering approach was introduced by Caruana et al. (2006) in <doi:10.1109/ICDM.2006.103>. Meta clustering involves generating a wide range of cluster solutions by adjusting clustering hyperparameters, then clustering the solutions themselves into a manageable number of qualitatively similar solutions, and finally characterizing representative solutions to find ones that are best for the user's specific context. This package provides a framework to easily transform multi-modal data into a wide range of similarity network fusion-derived cluster solutions as well as to visualize, characterize, and validate those solutions. Core package functionality includes easy customization of distance metrics, clustering algorithms, and SNF hyperparameters to generate diverse clustering solutions; calculation and plotting of associations between features, between patients, and between cluster solutions; and standard cluster validation approaches including resampled measures of cluster stability, standard metrics of cluster quality, and label propagation to evaluate generalizability in unseen data. Associated vignettes guide the user through using the package to identify patient subtypes while adhering to best practices for unsupervised learning.

Prashanth S Velayudhan

metasnf

Meta Clustering with Similarity Network Fusion

Xiaoqiao Xu

Prajkta Kallurkar

Ana Patricia Balbon

Maria T Secara

Adam Taback

Denise Sabac

Nicholas Chan

Shihao Ma

Bo Wang

Daniel Felsky

Stephanie H Ameis

Brian Cox

Colin Hawco

Lauren Erdman

Anne L Wheeler

income function


<div class="section" id="income">
<h3><code>income</code></h3><p></p><p>
</p><p>A data frame with 300 rows and 2 columns:</p><dl>
<dt>unique_id</dt>
<dd><p>The unique identifier of the ABCD dataset</p></dd><p></p><p><dt>household_income</dt>
<dd><p>Household income in 3 category levels (low = 1, medium = 2, high = 3)</p></dd></p><p>
</p></dl><p></p><p></p></div><p></p><p><div class="section" id="income">
<h3><code>income</code></h3></div></p><p>
</p><p>A data frame with 300 rows and 2 columns:</p><dl>
<dt>unique_id</dt>
<dd><p>The unique identifier of the ABCD dataset</p></dd><p></p><p><dt>household_income</dt>
<dd><p>Household income in 3 category levels (low = 1, medium = 2, high = 3)</p></dd></p><p>
</p></dl><p></p><p>
</p>

Format

Mock ABCD income data — income


<div class='section' id='income'>
<h3><code>income</code></h3>


<p>A data frame with 300 rows and 2 columns:</p><dl>
<dt>unique_id</dt>
<dd><p>The unique identifier of the ABCD dataset</p></dd>

<dt>household_income</dt>
<dd><p>Household income in 3 category levels (low = 1, medium = 2, high = 3)</p></dd>


</dl>

</div>

<div class='section' id='income'>
<h3><code>income</code></h3>


<p>A data frame with 300 rows and 2 columns:</p><dl>
<dt>unique_id</dt>
<dd><p>The unique identifier of the ABCD dataset</p></dd>

<dt>household_income</dt>
<dd><p>Household income in 3 category levels (low = 1, medium = 2, high = 3)</p></dd>


</dl>

</div>


Mock ABCD income data

income: Mock ABCD income data

Description

Usage

Arguments

Format

`income`

`income`