sparsediscrim

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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.

The package was archived in 2018 and was re-released in 2021. The package code was forked from John Ramey’s repo and subsequently modified.

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('topepo/sparsediscrim')

Usage

The formula and non-formula interfaces can be used:

library(sparsediscrim)

data(parabolic, package = "modeldata")

qda_mod <- qda_shrink_mean(class ~ ., data = parabolic)
# or
qda_mod <- qda_shrink_mean(x = parabolic[, 1:2], y = parabolic$class)

qda_mod
#> Shrinkage-Mean-Based Diagonal QDA
#> 
#> Sample Size: 500 
#> Number of Features: 2 
#> 
#> Classes and Prior Probabilities:
#>   Class1 (48.8%), Class2 (51.2%)

# Prediction uses the `type` argument: 

parabolic_grid <-
   expand.grid(X1 = seq(-5, 5, length = 100),
               X2 = seq(-5, 5, length = 100))


parabolic_grid$qda <- predict(qda_mod, parabolic_grid, type = "prob")$Class1

library(ggplot2)
ggplot(parabolic, aes(x = X1, y = X2)) +
   geom_point(aes(col = class), alpha = .5) +
   geom_contour(data = parabolic_grid, aes(z = qda), col = "black", breaks = .5) +
   theme_bw() +
   theme(legend.position = "top") +
   coord_equal()

Classifiers

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

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: