RFCCA-package: RFCCA: A package for computing canonical correlations...

In RFCCA: Random Forest with Canonical Correlation Analysis

RFCCA: A package for computing canonical correlations depending on subject-related covariates with random forests

Description

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. 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 method is described in Alakus et al. (2021).

RFCCA functions

rfcca predict.rfcca global.significance vimp.rfcca plot.vimp.rfcca print.rfcca

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.

Ishwaran, H., Kogalur, U. (2020). Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). R package version 2.9.3, https://cran.r-project.org/package=randomForestSRC.

RFCCA documentation built on Sept. 19, 2023, 9:06 a.m.