subset_matrix_to_df

Description of the convert matrices to data frame list for subnetworks function.

Analysis of network community objects with applications to neuroimaging data. There are two main components to this package. The first is the hierarchical multimodal spinglass (HMS) algorithm, which is a novel community detection algorithm specifically tailored to the unique issues within brain connectivity. The other is a suite of semiparametric kernel machine methods that allow for statistical inference to be performed to test for potential associations between these community structures and an outcome of interest (binary or continuous).

Alexandria Jensen

CommKern

Network-Based Communities and Kernel Machine Methods

Peter DeWitt

Debashis Ghosh

subset_matrix_to_df function

<dl><dt>func_matrix</dt>
<dd>a square, symmetric matrix to be used as the main input for
the <code>hms</code> algorithm. For brain connectivity, this will be a
representation of functional (e.g., BOLD) connectivity.</dd>
<dt>str_matrix</dt>
<dd>a square, symmetric matrix to be used as the guidance input
for the <code>hms</code> algorithm. For brain connectivity, this will be a
representation of structural (e.g., white matter) connectivity.</dd></dl>

Arguments

Convert matrices to list of data frames for subnetworks — subset_matrix_to_df

<dl>

<dt>func_matrix</dt>
<dd>a square, symmetric matrix to be used as the main input for
the <code>hms</code> algorithm. For brain connectivity, this will be a
representation of functional (e.g., BOLD) connectivity.</dd>


<dt>str_matrix</dt>
<dd>a square, symmetric matrix to be used as the guidance input
for the <code>hms</code> algorithm. For brain connectivity, this will be a
representation of structural (e.g., white matter) connectivity.</dd>

</dl>

subset_matrix_to_df: Convert matrices to list of data frames for subnetworks

Description

Usage

Value

Arguments

Details