CJIVE: Canonical Joint and Individual Variation Explained (CJIVE)
Joint and Individual Variation Explained (JIVE) is a method for decomposing multiple datasets obtained on the same subjects intoshared structure, structure unique to each dataset, and noise. The two most common implementations are R.JIVE, an iterativeapproach, and AJIVE, which uses principal angle analysis. JIVE estimates subspaces but interpreting these subspaces can bechallenging with AJIVE or R.JIVE. We expand upon insights into AJIVE as a canonical correlation analysis (CCA) of principal componentscores. This reformulation, which we call CJIVE, 1) provides an ordering of joint components by the degree of correlation betweencorresponding canonical variables; 2) uses a computationally efficient permutation test for the number of joint components, whichprovides 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>.
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[aut, cre], Benjamin Risk [aut]