Covariance regression with random forests

BMC Bioinformatics

Research

Denis Larocque¹  Aurélie Labbe¹  Cansu Alakus¹ 

[1] Department of Decision Sciences, HEC Montréal, Montréal, Canada;

关键词: Covariance regression;    Multivariate response;    Random forests;    Variable importance;   

DOI  :  10.1186/s12859-023-05377-y

 received in 2022-12-08, accepted in 2023-06-02,  发布年份 2023

来源: Springer

【 摘 要 】

Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine. We propose a new method called Covariance Regression with Random Forests (CovRegRF) to estimate the covariance matrix of a multivariate response given a set of covariates, using a random forest framework. Random forest trees are built with a splitting rule specially designed to maximize the difference between the sample covariance matrix estimates of the child nodes. We also propose a significance test for the partial effect of a subset of covariates. We evaluate the performance of the proposed method and significance test through a simulation study which shows that the proposed method provides accurate covariance matrix estimates and that the Type-1 error is well controlled. An application of the proposed method to thyroid disease data is also presented. CovRegRF is implemented in a freely available R package on CRAN.

【 授权许可】

CC BY   
© The Author(s) 2023

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