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.
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. <DOI:10.1214/09-AOAS277>
Zuber, V., and K. Strimmer. 2009. Gene ranking and biomarker discovery under correlation. Bioinformatics 25: 2700-2707. <DOI:10.1093/bioinformatics/btp460>
See website: https://strimmerlab.github.io/software/sda/