Package: sparsediscrim 0.2.5.9000

sparsediscrim: Sparse and Regularized Discriminant Analysis

A collection of sparse and regularized discriminant analysis methods intended for small-sample, high-dimensional data sets. The package features the High-Dimensional Regularized Discriminant Analysis classifier from Ramey et al. (2017) <arxiv:1602.01182>. Other classifiers include those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012) <doi:10.1093/bioinformatics/btr690>.

Authors:John A. Ramey <[email protected]>

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manual.pdf |manual.html✨
card.svg |card.png
sparsediscrim/json (API)
NEWS

# Install 'sparsediscrim' in R:

install.packages('sparsediscrim', repos = c('https://topepo.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/topepo/sparsediscrim/issues

Datasets:

two_class_sim_data - Example bivariate classification data from caret

On CRAN:

5.18 score 3 stars 1 packages 101 scripts 3.4k downloads 27 exports 28 dependencies

Last updated from:60198a54e0. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	134
source / vignettes	OK	197
linux-release-x86_64	OK	129
macos-release-arm64	OK	97
macos-oldrel-arm64	OK	93
windows-devel	OK	111
windows-release	OK	112
windows-oldrel	OK	81
wasm-release	OK	93

Exports:cov_autocorrelation cov_block_autocorrelation cov_eigen cov_list cov_mle cov_pool cov_shrink_diag cv_partition generate_blockdiag generate_intraclass lda_diag lda_eigen lda_emp_bayes lda_emp_bayes_eigen lda_pseudo lda_schafer lda_shrink_cov lda_shrink_mean lda_thomaz log_determinant qda_diag qda_shrink_cov qda_shrink_mean rda_high_dim rda_high_dim_cv solve_chol tong_mean_shrinkage

Dependencies:bdsmatrix cli corpcor cpp11 dplyr farver generics ggplot2 glue gtable isoband labeling lifecycle magrittr mvtnorm pillar pkgconfig R6 RColorBrewer rlang S7 scales tibble tidyselect utf8 vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Centers the observations in a matrix by their respective class sample means	center_data
Generates a p \times p autocorrelated covariance matrix	cov_autocorrelation
Generates a p \times p block-diagonal covariance matrix with autocorrelated blocks.	cov_block_autocorrelation
Computes the eigenvalue decomposition of the maximum likelihood estimators (MLE) of the covariance matrices for the given data matrix	cov_eigen
Generates a p \times p intraclass covariance matrix	cov_intraclass
Computes the covariance-matrix maximum likelihood estimators for each class and returns a list.	cov_list
Computes the maximum likelihood estimator for the sample covariance matrix under the assumption of multivariate normality.	cov_mle
Computes the pooled maximum likelihood estimator (MLE) for the common covariance matrix	cov_pool
Computes a shrunken version of the maximum likelihood estimator for the sample covariance matrix under the assumption of multivariate normality.	cov_shrink_diag
Randomly partitions data for cross-validation.	cv_partition
Computes estimates and ancillary information for diagonal classifiers	diag_estimates
Computes multivariate normal density with a diagonal covariance matrix	dmvnorm_diag
Generates data from 'K' multivariate normal data populations, where each population (class) has a covariance matrix consisting of block-diagonal autocorrelation matrices.	generate_blockdiag
Generates data from 'K' multivariate normal data populations, where each population (class) has an intraclass covariance matrix.	generate_intraclass
Bias correction function from Pang et al. (2009).	h
Diagonal Linear Discriminant Analysis (DLDA)	lda_diag lda_diag.default lda_diag.formula predict.lda_diag
The Minimum Distance Rule using Moore-Penrose Inverse (MDMP) classifier	lda_eigen lda_eigen.default lda_eigen.formula predict.lda_eigen
The Minimum Distance Empirical Bayesian Estimator (MDEB) classifier	lda_emp_bayes lda_emp_bayes.default lda_emp_bayes.formula predict.lda_emp_bayes
The Minimum Distance Rule using Modified Empirical Bayes (MDMEB) classifier	lda_emp_bayes_eigen lda_emp_bayes_eigen.default lda_emp_bayes_eigen.formula predict.lda_emp_bayes_eigen
Linear Discriminant Analysis (LDA) with the Moore-Penrose Pseudo-Inverse	lda_pseudo lda_pseudo.default lda_pseudo.formula predict.lda_pseudo
Linear Discriminant Analysis using the Schafer-Strimmer Covariance Matrix Estimator	lda_schafer lda_schafer.default lda_schafer.formula predict.lda_schafer
Shrinkage-based Diagonal Linear Discriminant Analysis (SDLDA)	lda_shrink_cov lda_shrink_cov.default lda_shrink_cov.formula predict.lda_shrink_cov
Shrinkage-mean-based Diagonal Linear Discriminant Analysis (SmDLDA) from Tong, Chen, and Zhao (2012)	lda_shrink_mean lda_shrink_mean.default lda_shrink_mean.formula predict.lda_shrink_mean
Linear Discriminant Analysis using the Thomaz-Kitani-Gillies Covariance Matrix Estimator	lda_thomaz lda_thomaz.default lda_thomaz.formula predict.lda_thomaz
Computes the log determinant of a matrix.	log_determinant
Removes the intercept term from a formula if it is included	no_intercept
Plots a heatmap of cross-validation error grid for a HDRDA classifier object.	plot.rda_high_dim_cv
Computes posterior probabilities via Bayes Theorem under normality	posterior_probs
Diagonal Quadratic Discriminant Analysis (DQDA)	predict.qda_diag qda_diag qda_diag.default qda_diag.formula
Shrinkage-based Diagonal Quadratic Discriminant Analysis (SDQDA)	predict.qda_shrink_cov qda_shrink_cov qda_shrink_cov.default qda_shrink_cov.formula
Shrinkage-mean-based Diagonal Quadratic Discriminant Analysis (SmDQDA) from Tong, Chen, and Zhao (2012)	predict.qda_shrink_mean qda_shrink_mean qda_shrink_mean.default qda_shrink_mean.formula
Quadratic form of a matrix and a vector	quadform
Quadratic Form of the inverse of a matrix and a vector	quadform_inv
Calculates the RDA covariance-matrix estimators for each class	rda_cov
High-Dimensional Regularized Discriminant Analysis (HDRDA)	predict.rda_high_dim rda_high_dim rda_high_dim.default rda_high_dim.formula
Helper function to optimize the HDRDA classifier via cross-validation	rda_high_dim_cv
Computes the observation weights for each class for the HDRDA classifier	rda_weights
Computes estimates and ancillary information for regularized discriminant classifiers	regdiscrim_estimates
Stein Risk function from Pang et al. (2009).	risk_stein
Computes the inverse of a symmetric, positive-definite matrix using the Cholesky decomposition	solve_chol
Tong et al. (2012)'s Lindley-type Shrunken Mean Estimator	tong_mean_shrinkage
Example bivariate classification data from caret	two_class_sim_data
Helper function to update tuning parameters for the HDRDA classifier	update_rda_high_dim
Shrinkage-based estimator of variances for each feature from Pang et al. (2009).	var_shrinkage