Melt.Sim.Cors

Converts correlations of predicted to true joint subject scores into a format conducive to ggplot2

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

Melt.Sim.Cors function

<dl><dt>sim.dat</dt>
<dd>matrix with each row representing results from a replicate in the simulation study described in CJIVE manuscript</dd>
<dt>r.J</dt>
<dd>(Numeric/integer) the joint rank, i.e. number of components in the joint subspace</dd>
<dt>p1</dt>
<dd>number of variables/features in data set X1</dd>
<dt>p2</dt>
<dd>number of variables/features in data set X2</dd></dl>

Arguments

Converts correlations of predicted to true joint subject scores to a format conducive to ggplot2 — Melt.Sim.Cors

<dl>

<dt>sim.dat</dt>
<dd>matrix with each row representing results from a replicate in the simulation study described in CJIVE manuscript</dd>


<dt>r.J</dt>
<dd>(Numeric/integer) the joint rank, i.e. number of components in the joint subspace</dd>


<dt>p1</dt>
<dd>number of variables/features in data set X1</dd>


<dt>p2</dt>
<dd>number of variables/features in data set X2</dd>

</dl>

Melt.Sim.Cors: Converts correlations of predicted to true joint subject scores to a format conducive to ggplot2

Description

Usage

Value

Arguments