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

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>

removePrincipalComponents: Remove leading principal components from data

Description

Usage

Value

Arguments

Author

See Also