Man pages for mdkessler/kTSCR
Implements k-Top Scoring Cluster Regression
| all_equal | Check vector items for equality |
| app_minus_test_thresh_weighted_sum | Weight feature importance sum on the basis of overfitting |
| calc_K_from_pairs | Calculate K score from pairs |
| choose_top_features | Choose top features |
| condense_k_cv_output | Condense k cross validation output |
| convert_sibling_names_to_indices | Convert sibling names to indices |
| elder_weighted_corr_score | Calculate weighted corr score |
| get_app_minus_test | Calculate apparent correlation minus test correlation |
| get_elder | Calculate weighted feature correlation score |
| get_pairwise_rank_matrices | Calculate feature pair scores |
| get_siblings | Determine elder and siblings that make cluster |
| get_sorted_corrs_pairwise_features | Correlations between each pairwise feature and y |
| get_top_clusters | Implements kTSCR algorithm |
| make_feature_pair_score_matrix | Combine per sample feature pair score matrices into a single... |
| predict | Predict y from K |
| rmse | Calculate Root Mean Squared Error |
| run_cross_validation | Run k fold cross validation |
| split_sibling_indices | Split pairwise features into numeric indices |
| split_sibling_names | Split pairwise features into character vector |
| update_pairwise_feature_mat | Remove elder from pairwise feature matrix |
