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CJIVE  

Canonical Joint and Individual Variation Explained (CJIVE)
View on CRAN: Click here


Download and install CJIVE package within the R console
Install from CRAN:
install.packages("CJIVE")

Install from Github:
library("remotes")
install_github("cran/CJIVE")

Install by package version:
library("remotes")
install_version("CJIVE", "0.1.0")



Attach the package and use:
library("CJIVE")
Maintained by
Raphiel Murden
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-01-20
Latest Update: 2023-01-20
Description:
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) .
How to cite:
Raphiel Murden (2023). CJIVE: Canonical Joint and Individual Variation Explained (CJIVE). R package version 0.1.0, https://cran.r-project.org/web/packages/CJIVE. Accessed 29 Jan. 2025.
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