create.graph.long

Convert matrix representation of a network for graphical display via ggplot

Joint and Individual Variation Explained (JIVE) is a method for decomposing multiple datasets obtained on the same subjects into
shared structure, structure unique to each dataset, and noise. The two most common implementations are R.JIVE, an iterative
approach, and AJIVE, which uses principal angle analysis. JIVE estimates subspaces but interpreting these subspaces can be
challenging with AJIVE or R.JIVE. We expand upon insights into AJIVE as a canonical correlation analysis (CCA) of principal component
scores. This reformulation, which we call CJIVE, 1) provides an ordering of joint components by the degree of correlation between
corresponding canonical variables; 2) uses a computationally efficient permutation test for the number of joint components, which
provides a p-value for each component; and 3) can be used to predict subject scores for out-of-sample observations.
Please cite the following article when utilizing this package:
Murden, R., Zhang, Z., Guo, Y., & Risk, B. (2022) <doi:10.3389/fnins.2022.969510>.

Raphiel Murden

CJIVE

Canonical Joint and Individual Variation Explained (CJIVE)

Benjamin Risk

create.graph.long function

<dl><dt>gmatrix</dt>
<dd>square matrix of size p-by-p in which entries represent the strength of (un-directed) edges between the p nodes</dd>
<dt>sort_indices</dt>
<dd>vector of length p by which nodes are sorted. If NULL, then nodes are not sorted. Default is NULL.</dd></dl>

Arguments

Function for plotting networks with ggplot — create.graph.long

<dl>

<dt>gmatrix</dt>
<dd>square matrix of size p-by-p in which entries represent the strength of (un-directed) edges between the p nodes</dd>


<dt>sort_indices</dt>
<dd>vector of length p by which nodes are sorted. If NULL, then nodes are not sorted. Default is NULL.</dd>

</dl>

create.graph.long: Function for plotting networks with ggplot

Description

Usage

Value

Arguments