In topepo/sparsediscrim: Sparse and Regularized Discriminant Analysis

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sparsediscrim

The R package sparsediscrim provides a collection of sparse and regularized discriminant analysis classifiers that are especially useful for when applied to small-sample, high-dimensional data sets.

Installation

You can install the stable version on CRAN:

install.packages('sparsediscrim', dependencies = TRUE)

If you prefer to download the latest version, instead type:

library(devtools)
install_github('ramhiser/sparsediscrim')

Classifiers

The sparsediscrim package features the following classifier (the R function is included within parentheses):

• High-Dimensional Regularized Discriminant Analysis (rda_high_dim) from Ramey et al. (2015)

The sparsediscrim package also includes a variety of additional classifiers intended for small-sample, high-dimensional data sets. These include:

| Classifier | Author | R Function | |---------------------------------------------------------------|----------------------------------------------------------------------------------------------------|------------| | Diagonal Linear Discriminant Analysis | Dudoit et al. (2002) | lda_diag | | Diagonal Quadratic Discriminant Analysis | Dudoit et al. (2002) | qda_diag | | Shrinkage-based Diagonal Linear Discriminant Analysis | Pang et al. (2009) | lda_shrink_cov | | Shrinkage-based Diagonal Quadratic Discriminant Analysis | Pang et al. (2009) | qda_shrink_cov | | Shrinkage-mean-based Diagonal Linear Discriminant Analysis | Tong et al. (2012) | lda_shrink_mean | | Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis | Tong et al. (2012) | qda_shrink_mean | | Minimum Distance Empirical Bayesian Estimator (MDEB) | Srivistava and Kubokawa (2007) | lda_emp_bayes | | Minimum Distance Rule using Modified Empirical Bayes (MDMEB) | Srivistava and Kubokawa (2007) | lda_emp_bayes_eigen | | Minimum Distance Rule using Moore-Penrose Inverse (MDMP) | Srivistava and Kubokawa (2007) | lda_eigen |

We also include modifications to Linear Discriminant Analysis (LDA) with regularized covariance-matrix estimators:

• Moore-Penrose Pseudo-Inverse (lda_pseudo)
• Schafer-Strimmer estimator (lda_schafer)
• Thomaz-Kitani-Gillies estimator (lda_thomaz)

topepo/sparsediscrim documentation built on Dec. 23, 2021, 11:59 a.m.