Automated Multicollinearity Management

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Documentation for package ‘collinear’ version 2.0.0

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add_white_noise Add White Noise to Encoded Predictor
case_weights Case Weights for Unbalanced Binomial or Categorical Responses
collinear Automated multicollinearity management
cor_categorical_vs_categorical Pairwise Correlation Data Frame
cor_clusters Hierarchical Clustering from a Pairwise Correlation Matrix
cor_cramer_v Bias Corrected Cramer's V
cor_df Pairwise Correlation Data Frame
cor_matrix Pairwise Correlation Matrix
cor_numeric_vs_categorical Pairwise Correlation Data Frame
cor_numeric_vs_numeric Pairwise Correlation Data Frame
cor_select Automated Multicollinearity Filtering with Pairwise Correlations
drop_geometry_column Removes geometry column in sf data frames
encoded_predictor_name Name of Target-Encoded Predictor
f_auc Association Between a Binomial Response and a Continuous Predictor
f_auc_gam_binomial Association Between a Binomial Response and a Continuous Predictor
f_auc_glm_binomial Association Between a Binomial Response and a Continuous Predictor
f_auc_glm_binomial_poly2 Association Between a Binomial Response and a Continuous Predictor
f_auc_rf Association Between a Binomial Response and a Continuous Predictor
f_auc_rpart Association Between a Binomial Response and a Continuous Predictor
f_auto Select Function to Compute Preference Order
f_auto_rules Rules to Select Default f Argument to Compute Preference Order
f_functions Data Frame of Preference Functions
f_r2 Association Between a Continuous Response and a Continuous Predictor
f_r2_counts Association Between a Count Response and a Continuous Predictor
f_r2_gam_gaussian Association Between a Continuous Response and a Continuous Predictor
f_r2_gam_poisson Association Between a Count Response and a Continuous Predictor
f_r2_glm_gaussian Association Between a Continuous Response and a Continuous Predictor
f_r2_glm_gaussian_poly2 Association Between a Continuous Response and a Continuous Predictor
f_r2_glm_poisson Association Between a Count Response and a Continuous Predictor
f_r2_glm_poisson_poly2 Association Between a Count Response and a Continuous Predictor
f_r2_pearson Association Between a Continuous Response and a Continuous Predictor
f_r2_rf Association Between a Continuous Response and a Continuous Predictor
f_r2_rpart Association Between a Continuous Response and a Continuous Predictor
f_r2_spearman Association Between a Continuous Response and a Continuous Predictor
f_v Association Between a Categorical Response and a Categorical Predictor
f_v_rf_categorical Association Between a Categorical Response and a Categorical or Numeric Predictor
identify_predictors Identify Numeric and Categorical Predictors
identify_predictors_categorical Identify Valid Categorical Predictors
identify_predictors_numeric Identify Valid Numeric Predictors
identify_predictors_type Identify Predictor Types
identify_predictors_zero_variance Identify Zero and Near-Zero Variance Predictors
identify_response_type Identify Response Type
model_formula Generate Model Formulas
performance_score_auc Area Under the Curve of Binomial Observations vs Probabilistic Model Predictions
performance_score_r2 Pearson's R-squared of Observations vs Predictions
performance_score_v Cramer's V of Observations vs Predictions
preference_order Quantitative Variable Prioritization for Multicollinearity Filtering
preference_order_collinear Preference Order Argument in collinear()
target_encoding_lab Target Encoding Lab: Transform Categorical Variables to Numeric
target_encoding_loo Target Encoding Methods
target_encoding_mean Target Encoding Methods
target_encoding_rank Target Encoding Methods
toy One response and four predictors with varying levels of multicollinearity
validate_data_cor Validate Data for Correlation Analysis
validate_data_vif Validate Data for VIF Analysis
validate_df Validate Argument df
validate_encoding_arguments Validates Arguments of 'target_encoding_lab()'
validate_predictors Validate Argument predictors
validate_preference_order Validate Argument preference_order
validate_response Validate Argument response
vi Example Data With Different Response and Predictor Types
vif_df Variance Inflation Factor
vif_select Automated Multicollinearity Filtering with Variance Inflation Factors
vi_predictors All Predictor Names in Example Data Frame vi
vi_predictors_categorical All Categorical and Factor Predictor Names in Example Data Frame vi
vi_predictors_numeric All Numeric Predictor Names in Example Data Frame vi