removePrincipalComponents

This function calculates a fixed number of the first principal components of the given data and returns the
residuals of a linear regression of each column on the principal components.

misc

Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.

Peter Langfelder

WGCNA

Weighted Correlation Network Analysis

Steve Horvath

Chaochao Cai

Jun Dong

Jeremy Miller

Lin Song

Andy Yip

Bin Zhang

removePrincipalComponents function

<dl> <dt>x</dt>
<dd>Input data, a numeric matrix. All entries must be non-missing and finite.</dd> <dt>n</dt>
<dd>Number of principal components to remove. This must be smaller than the smaller of the number of rows and
columns in <code>x</code>.</dd></dl>

Arguments

Author

Remove leading principal components from data — removePrincipalComponents

<dl>

 <dt>x</dt>
<dd>Input data, a numeric matrix. All entries must be non-missing and finite.</dd>

 <dt>n</dt>
<dd>Number of principal components to remove. This must be smaller than the smaller of the number of rows and
columns in <code>x</code>.</dd>

</dl>

Remove leading principal components from data

removePrincipalComponents: Remove leading principal components from data

Description

Usage

Value

Arguments

Author

See Also