Identification of important regressor groups, subgroups and individuals via regularization methods: application to gut microbiome data.

ABSTRACT:

Motivation

Gut microbiota can be classified at multiple taxonomy levels. Strategies to use changes in microbiota composition to effect health improvements require knowing at which taxonomy level interventions should be aimed. Identifying these important levels is difficult, however, because most statistical methods only consider when the microbiota are classified at one taxonomy level, not multiple.

Results

Using L1 and L2 regularizations, we developed a new variable selection method that identifies important features at multiple taxonomy levels. The regularization parameters are chosen by a new, data-adaptive, repeated cross-validation approach, which performed well. In simulation studies, our method outperformed competing methods: it more often selected significant variabl

SUBMITTER: Garcia TP 

PROVIDER: S-EPMC3957069 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature