Automated Multicollinearity Management

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Documentation for package ‘collinear’ version 3.0.0

• DESCRIPTION file.
• Package NEWS.

Help Pages

bake.step_collinear	Tidymodels recipe step for multicollinearity filtering
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 selection of multicollinearity thresholds
experiment_cor_vs_vif	Dataframe with results of experiment comparing correlation and VIF thresholds
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. observations
f_binomial_glm	Area Under the Curve of Binomial GLM predictions vs. observations
f_binomial_rf	Area Under the Curve of Binomial Random Forest predictions vs. observations
f_categorical_rf	Cramer's V of Categorical Random Forest predictions vs. observations
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 thresholds
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 dataframe
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 values
preference_order	Rank predictors by importance or multicollinearity
prep.step_collinear	Tidymodels recipe step for multicollinearity filtering
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 observations and probabilistic predictions
score_cramer	Compute Cramer's V between categorical observations and predictions
score_r2	Compute R-squared between numeric observations and predictions
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_loo	Encode categories as response means
target_encoding_mean	Encode categories as response means
target_encoding_rank	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 threshold
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