Random Forest with Canonical Correlation Analysis

Random Forest with Canonical Correlation Analysis (RFCCA) is a random forest method for estimating the canonical correlations between two sets of variables depending on the subject-related covariates. The trees are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. The method is described in Alakus et al. (2021) . 'RFCCA' uses 'randomForestSRC' package (Ishwaran and Kogalur, 2020) by freezing at the version 2.9.3. The custom splitting rule feature is utilised to apply the proposed splitting rule. The 'randomForestSRC' package implements 'OpenMP' by default, contingent upon the support provided by the target architecture and operating system. In this package, 'LAPACK' and 'BLAS' libraries are used for matrix decompositions.

RFCCA

R package which implements Random Forest with Canonical Correlation Analysis (RFCCA).

RFCCA is a random forest method for estimating the canonical correlations between two sets of variables, X and Y, depending on the subject-related covariates, Z. The trees are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes.

For theoretical details and example data analysis, you can look at the vignette from within R by using the following command:

vignette("RFCCA")

Installation

This package is available on CRAN. Alternatively, you can install RFCCA from GitHub using the devtools package. Run the following code in R to install:

if (!require(devtools)) {
  install.packages("devtools")
  library(devtools)
}
devtools::install_github('calakus/RFCCA', build_vignettes = TRUE)

References

• Alakus, C., Larocque, D., Jacquemont, S., Barlaam, F., Martin, C.-O., Agbogba, K., Lippe, S., and Labbe, A. (2021). Conditional canonical correlation estimation based on covariates with random forests. Bioinformatics, 37(17), 2714-2721. doi:10.1093/bioinformatics/btab158.