Sparse and Regularized Discriminant Analysis

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Documentation for package ‘sparsediscrim’ version 0.3.0

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