Man pages for collinear
Automated Multicollinearity Management
| case_weights | Generate sample weights for imbalanced responses |
| collinear | Smart multicollinearity management |
| collinear_select | Dual multicollinearity filtering algorithm |
| collinear_stats | Compute summary statistics for correlation and VIF |
| cor_clusters | Group predictors by hierarchical correlation clustering |
| cor_cramer | Quantify association between categorical variables |
| cor_df | Compute signed pairwise correlations dataframe |
| cor_matrix | Signed pairwise correlation matrix |
| cor_select | Multicollinearity filtering by pairwise correlation threshold |
| cor_stats | Compute summary statistics for absolute pairwise correlations |
| drop_geometry_column | Removes 'geometry' Column From 'sf' Dataframes |
| experiment_adaptive_thresholds | Dataframe resulting from experiment to test the automatic... |
| experiment_cor_vs_vif | Dataframe with results of experiment comparing correlation... |
| f_auto | Automatic selection of predictor scoring method |
| f_auto_rules | Decision rules for 'f_auto()' |
| f_binomial_gam | Area under the curve of binomial GAM predictions vs.... |
| f_binomial_glm | Area Under the Curve of Binomial GLM predictions vs.... |
| f_binomial_rf | Area Under the Curve of Binomial Random Forest predictions... |
| f_categorical_rf | Cramer's V of Categorical Random Forest predictions vs.... |
| f_count_gam | R-squared of Poisson GAM predictions vs. observations |
| f_count_glm | R-squared of Poisson GLM predictions vs. observations |
| f_count_rf | R-squared of Random Forest predictions vs. observations |
| f_functions | List predictor scoring functions |
| f_numeric_gam | R-squared of Gaussian GAM predictions vs. observations |
| f_numeric_glm | R-squared of Gaussian GLM predictions vs. observations |
| f_numeric_rf | R-squared of Random Forest predictions vs. observations |
| gam_cor_to_vif | GAM describing the relationship between correlation and VIF... |
| identify_categorical_variables | Find valid categorical variables in a dataframe |
| identify_logical_variables | Find logical variables in a dataframe |
| identify_numeric_variables | Find valid numeric variables in a dataframe |
| identify_response_type | Detect response variable type for model selection |
| identify_valid_variables | Find valid numeric, categorical, and logical variables in a... |
| identify_zero_variance_variables | Find near-zero variance variables in a dataframe |
| model_formula | Build model formulas from response and predictors |
| prediction_cor_to_vif | Prediction of the model 'gam_cor_to_vif' across correlation... |
| preference_order | Rank predictors by importance or multicollinearity |
| print.collinear_output | Print all collinear selection results of 'collinear()' |
| print.collinear_selection | Print single selection results from 'collinear' |
| score_auc | Compute area under the ROC curve between binomial... |
| score_cramer | Compute Cramer's V between categorical observations and... |
| score_r2 | Compute R-squared between numeric observations and... |
| step_collinear | Tidymodels recipe step for multicollinearity filtering |
| summary.collinear_output | Summarize all results of 'collinear()' |
| summary.collinear_selection | Summarize single response selection results of 'collinear' |
| target_encoding_lab | Convert categorical predictors to numeric via target encoding |
| target_encoding_methods | Encode categories as response means |
| toy | Toy dataframe with varying levels of multicollinearity |
| validate_arg_df | Check and prepare argument 'df' |
| validate_arg_df_not_null | Ensure that argument 'df' is not 'NULL' |
| validate_arg_encoding_method | Check and validate argument 'encoding_method' |
| validate_arg_f | Check and validate argument 'f' |
| validate_arg_function_name | Build hierarchical function names for messages |
| validate_arg_max_cor | Check and constrain argument 'max_cor' |
| validate_arg_max_vif | Check and constrain argument 'max_vif' |
| validate_arg_predictors | Check and validate argument 'predictors' |
| validate_arg_preference_order | Check and complete argument 'preference_order' |
| validate_arg_quiet | Check and validate argument 'quiet' |
| validate_arg_responses | Check and validate arguments 'response' and 'responses' |
| vi | Large example dataframe |
| vif | Compute variance inflation factors from a correlation matrix |
| vif_df | Compute variance inflation factors dataframe |
| vif_select | Multicollinearity filtering by variance inflation factor... |
| vif_stats | VIF Statistics |
| vi_predictors | Vector of all predictor names in 'vi' and 'vi_smol' |
| vi_predictors_categorical | Vector of categorical predictors in 'vi' and 'vi_smol' |
| vi_predictors_numeric | Vector of numeric predictor names in 'vi' and 'vi_smol' |
| vi_responses | Vector of response names in 'vi' and 'vi_smol' |
| vi_smol | Small example dataframe |
