Description

This package performs linear discriminant analysis (LDA) and diagonal discriminant analysis (DDA) with variable selection using correlation-adjusted t (CAT) scores.

The classifier is trained using James-Stein-type shrinkage estimators. Variable selection is based on ranking predictors by CAT scores (LDA) or t-scores (DDA). A cutoff is chosen by false non-discovery rate (FNDR) or higher criticism (HC) thresholding.

This approach is particularly suited for high-dimensional classification with correlation among predictors. For details see Zuber and Strimmer (2009) and Ahdesm\"aki and Strimmer (2010).

Typically the functions in this package are applied in three steps:

• Step 1:feature selection with sda.ranking,

• Step 2:training the classifier with sda, and

• Step 3:classification using predict.sda.

The accompanying web site (see below) provides example R scripts to illustrate the functionality of this package.

Author(s)

Miika Ahdesm\"aki, Verena Zuber and Korbinian Strimmer (http://strimmerlab.org/)

References

Ahdesm\"aki, A., and K. Strimmer. 2010. Feature selection in omics prediction problems using cat scores and false non-discovery rate control. Ann. Appl. Stat. 4: 503-519. Preprint available from http://arxiv.org/abs/0903.2003.

Zuber, V., and K. Strimmer. 2009. Gene ranking and biomarker discovery under correlation. Bioinformatics 25: 2700-2707. Preprint available from http://arxiv.org/abs/0902.0751.

See website: http://strimmerlab.org/software/sda/

See Also

catscore, sda.ranking, sda, predict.sda.