Package index • baitlist

All functions

compare_models(): Compare feature importance of models using the Wald test
create_criteria_table(): Create Criteria table
create_group_comparison_tables(): Create group comparison table
create_model_weights_tables(): Create Model Weights tables
create_pooled_group_comparison_tables(): Create pooled group comparison table (interaction models)
feature_importance(): Calculates feature importance for all BAIT model types and groups using standardized coefficients and maximum utility contribution.
feature_importance_all(): Calculates feature importance for all BAIT model types and groups using standardized coefficients and maximum utility contribution.
fit_bait_binary(): Fits the responses in a BAIT binary logistic regression model for each respondent group
fit_bait_multinomial(): Fits the responses in a BAIT multinomial logistic regression model for each respondent group
fit_pooled_interaction_binary(): Fits a pooled binary model with interaction effects using data from both group1 and group2 to allow comparing difference in coefficients between groups
fit_pooled_interaction_multinomial(): Fits a pooled multinomial model with interaction effects using data from both group1 and group2 to allow comparing difference in coefficients between groups
fit_pooled_models(): Fit pooled models with interaction effects for group-wise comparisons to allow comparing coefficients for Amsterdam UMC vs. OLVG and Intensivists vs. Fellows for both binary and multinomial models
load_responses(): Load responses Reads data from the data/responses.xlsx file for specific groups or all if not specified.
maximum_utility_contribution(): Calculate feature importance using maximum utility contribution.
plot_group_comparison(): Plots group comparison of coefficient weights
plot_pooled_group_comparison(): Plots group comparison of coefficient weights
plot_relative_importance(): Plot Relative Importance
predict(): Predict choice based on patient characteristics and family values
run_linearity_tests(): Runs linearity tests for multi-level ordered categories.
standardized_coefficients(): Calculate feature importance using standardized coefficients
wald_interaction(): Creates a dataframe containing the Joint Wald test and Wald tests for individual coefficients in a pooled interaction model of two groups