Seamless Multicollinearity Management

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Documentation for package ‘collinear’ version 1.1.1

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add_white_noise Target-encoding methods
auc_score Area Under the Receiver Operating Characteristic
collinear Automated multicollinearity management
cor_df Correlation data frame of numeric and character variables
cor_matrix Correlation matrix of numeric and character variables
cor_select Automated multicollinearity reduction via pairwise correlation
cramer_v Bias Corrected Cramer's V
f_gam_auc_balanced AUC of Logistic GAM Model
f_gam_auc_unbalanced AUC of Logistic GAM Model with Weighted Cases
f_gam_deviance Explained Deviance from univariate GAM model
f_logistic_auc_balanced AUC of Binomial GLM with Logit Link
f_logistic_auc_unbalanced AUC of Binomial GLM with Logit Link and Case Weights
f_rf_auc_balanced AUC of Random Forest model of a balanced binary response
f_rf_auc_unbalanced AUC of Random Forest model of an unbalanced binary response
f_rf_deviance R-squared of Random Forest model
f_rf_rsquared R-squared of Random Forest model
f_rsquared R-squared between a response and a predictor
identify_non_numeric_predictors Identify non-numeric predictors
identify_numeric_predictors Identify numeric predictors
identify_zero_variance_predictors Identify zero and near-zero-variance predictors
preference_order Compute the preference order for predictors based on a user-defined function.
target_encoding_lab Target encoding of non-numeric variables
target_encoding_loo Target-encoding methods
target_encoding_mean Target-encoding methods
target_encoding_rank Target-encoding methods
target_encoding_rnorm Target-encoding methods
toy One response and four predictors with varying levels of multicollinearity
validate_df Validate input data frame
validate_predictors Validate the 'predictors' argument for analysis
validate_response Validate the 'response' argument for target encoding of non-numeric variables
vi 30.000 records of responses and predictors all over the world
vif_df Variance Inflation Factor
vif_select Automated multicollinearity reduction via Variance Inflation Factor
vi_predictors Predictor names in data frame 'vi'