Covariance regression with random forests.
BMC Bioinformatics
; 24(1): 258, 2023 Jun 17.
Article
em En
| MEDLINE
| ID: mdl-37330468
ABSTRACT
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.
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Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
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Algoritmo Florestas Aleatórias
Tipo de estudo:
Clinical_trials
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Prognostic_studies
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Risk_factors_studies
Limite:
Child
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Humans
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article