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r / packages / r-subsamp

This subsample winner algorithm (SWA) for regression with a large-p data (X, Y) selects the important variables (or features) among the p features X in explaining the response Y. The SWA first uses a base procedure, here a linear regression, on each of subsamples randomly drawn from the p variables, and then computes the scores of all features, i.e., the p variables, according to the performance of these features collected in each of the subsample analyses. It then obtains the 'semifinalist' of the features based on the resulting scores and determines the 'finalists', i.e., the important features, from the 'semifinalist'. Fan, Sun and Qiao (2017) <http://sr2c.case.edu/swa-reg/>.

Type Size Name Uploaded Downloads Labels
conda 41.1 kB | noarch/r-subsamp-0.1.0-r43h142f84f_0.tar.bz2  1 year and 2 months ago 22 main
conda 40.5 kB | noarch/r-subsamp-0.1.0-r42h142f84f_0.tar.bz2  2 years and 9 months ago 50 main
conda 40.4 kB | noarch/r-subsamp-0.1.0-r36h6115d3f_0.tar.bz2  5 years and 23 days ago 127 main

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