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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>.
Version: | 0.1.0 |
Imports: | rootSolve, ggplot2, reshape2, fields, gplots, psych |
Suggests: | testthat (≥ 3.0.0) |
Published: | 2023-01-20 |
DOI: | 10.32614/CRAN.package.CJIVE |
Author: | Raphiel Murden |
Maintainer: | Raphiel Murden <rmurden at emory.edu> |
License: | MIT + file LICENSE |
NeedsCompilation: | no |
Citation: | CJIVE citation info |
Materials: | README |
CRAN checks: | CJIVE results |
Reference manual: | CJIVE.pdf |
Package source: | CJIVE_0.1.0.tar.gz |
Windows binaries: | r-devel: CJIVE_0.1.0.zip, r-release: CJIVE_0.1.0.zip, r-oldrel: CJIVE_0.1.0.zip |
macOS binaries: | r-release (arm64): CJIVE_0.1.0.tgz, r-oldrel (arm64): CJIVE_0.1.0.tgz, r-release (x86_64): CJIVE_0.1.0.tgz, r-oldrel (x86_64): CJIVE_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.