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)
