README.md

In sparsediscrim: Sparse and Regularized Discriminant Analysis

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.

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):

• 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())

sparsediscrim documentation built on July 1, 2021, 9:07 a.m.