gg.norm.plot

Graphically displays the center and spread of chordal norms for joint/individual score/loading subspaces

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

gg.norm.plot function

<dl><dt>norm.dat</dt>
<dd>data frame with at least the 5 following variables:
Norm - the value of the norm for a particular subspace;
Type - the subspace for which the norm is given (i.e., joint/individual score/loading for dataset X1 or X2 (except for joint scores))
Method - the method by which the subspace was estimated, e.g. CJIVE, AJIVE, R.JIVE
JVE_1 and JVE_2 - labels describing the proportion of joint variation explained in each dataset (and typically the number of variables in dataset X2)</dd>
<dt>cols</dt>
<dd>a vector of colors, must have length equal to the number of methods used in the simulation</dd>
<dt>show.legend</dt>
<dd>logical (TRUE/FALSE) for whether a legend should be included in the plot. Default is FALSE</dd>
<dt>text.size</dt>
<dd>numeric value for the font size</dd>
<dt>lty</dt>
<dd>linetype (see ggplot2). Default = 1</dd>
<dt>y.max</dt>
<dd>maximum value for the horizontal axis of the plot</dd>
<dt>x.lab.angle</dt>
<dd>angle at which x-axis labels are tilted</dd></dl>

Arguments

Function for plotting chordal norms between estimated and true subspaces within the simulation study described in CJIVE manuscript — gg.norm.plot

<dl>

<dt>norm.dat</dt>
<dd>data frame with at least the 5 following variables:
Norm - the value of the norm for a particular subspace;
Type - the subspace for which the norm is given (i.e., joint/individual score/loading for dataset X1 or X2 (except for joint scores))
Method - the method by which the subspace was estimated, e.g. CJIVE, AJIVE, R.JIVE
JVE_1 and JVE_2 - labels describing the proportion of joint variation explained in each dataset (and typically the number of variables in dataset X2)</dd>


<dt>cols</dt>
<dd>a vector of colors, must have length equal to the number of methods used in the simulation</dd>


<dt>show.legend</dt>
<dd>logical (TRUE/FALSE) for whether a legend should be included in the plot. Default is FALSE</dd>


<dt>text.size</dt>
<dd>numeric value for the font size</dd>


<dt>lty</dt>
<dd>linetype (see ggplot2). Default = 1</dd>


<dt>y.max</dt>
<dd>maximum value for the horizontal axis of the plot</dd>


<dt>x.lab.angle</dt>
<dd>angle at which x-axis labels are tilted</dd>

</dl>

gg.norm.plot: Function for plotting chordal norms between estimated and true subspaces within the simulation study described in CJIVE manuscript

Description

Usage

Value

Arguments