sodavis: SODA: Main and Interaction Effects Selection for Discriminant Analysis and Logistic Regression

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Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under quadratic discriminant analysis and logistic regression model.

Author
Yang Li, Jun S. Liu
Date of publication
2015-11-16 08:20:20
Maintainer
Yang Li <yli01@fas.harvard.edu>
License
GPL-2
Version
0.1

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Man pages

soda
SODA algorithm for variable and interaction selection
soda_trace_CV
Calculate a trace of cross-validation error rate for SODA...
umich_lung
Gene expression data for Michigan lung cancer study in Beer...

Files in this package

sodavis
sodavis/NAMESPACE
sodavis/data
sodavis/data/mich_lung.RData
sodavis/data/datalist
sodavis/R
sodavis/R/pure_soda.R
sodavis/MD5
sodavis/DESCRIPTION
sodavis/man
sodavis/man/soda.Rd
sodavis/man/soda_trace_CV.Rd
sodavis/man/umich_lung.Rd