High-Dimensional Mean Comparison with Projection and Cross-Fitting

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Documentation for package ‘HMC’ version 1.2

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anchored_lasso_testing Anchored test for two-sample mean comparison.
check_data_for_folds Check that data has enough rows for cross-validation folds
check_non_null_and_identical_colnames Check non-null and consistent column names across datasets
collect_active_features_proj Collect active features and groups based on projection directions
combine_folds_mean_diff Combine fold-level test statistics from cross-validation
compute_predictive_contributions Compute predictive contributions of feature groups
debiased_pc_testing Debiased one-step test for two-sample mean comparison. A small p-value tells us not only there is difference in the mean vectors, but can also indicates which principle component the difference aligns with.
estimate_leading_pc Estimate the leading principal component
estimate_nuisance_parameter_lasso The function for nuisance parameter estimation in anchored_lasso_testing().
estimate_nuisance_pc The function for nuisance parameter estimation in simple_pc_testing() and debiased_pc_testing().
evaluate_influence_function_multi_factor Calculate the test statistics on the left-out samples. Called in debiased_pc_testing().
evaluate_pca_lasso_plug_in Calculate the test statistics on the left-out samples. Called in anchored_lasso_testing().
evaluate_pca_plug_in Calculate the test statistics on the left-out samples. Called in simple_pc_testing().
extract_lasso_coef Extract the lasso estimate from the output of anchored_lasso_testing().
extract_pc Extract the principle components from the output of simple_pc_testing() and debiased_pc_testing().
fit_lasso Fit a (group) Lasso logistic regression classifier
index_spliter Split indices into folds
mean_comparison_anchor High-dimensional two-sample mean comparison with anchored projection
normalize_and_split Normalize and split two datasets using pooled mean and standard deviation
process_fold_mean_diff Process one cross-validation fold for mean difference testing
simple_pc_testing Simple plug-in test for two-sample mean comparison.
summarize_feature_name Summarize the features (e.g. genes) that contribute to the test result, i.e. those features consistently show up in Lasso vectors.
summarize_pc_name Summarize the features (e.g. genes) that contribute to the test result, i.e. those features consistently show up in the sparse principle components.
validate_and_convert_data Validate and convert input data