scale_loadings

Scale loadings for a joint or individual component by its largest absolute value resulting in loadings between -1 and 1.
 Loadings are also sign-corrected to result in positive skewness

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

scale_loadings function

<dl><dt>loading.comp</dt>
<dd>numeric vector of variable loadings from a JIVE analysis</dd></dl>

Arguments

Scale and sign-correct variable loadings to assist interpretation — scale_loadings

<dl>

<dt>loading.comp</dt>
<dd>numeric vector of variable loadings from a JIVE analysis</dd>

</dl>

scale_loadings: Scale and sign-correct variable loadings to assist interpretation

Description

Usage

Value

Arguments