## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE ) ## ----setup-------------------------------------------------------------------- library(LogRegEquiv) ## ----model construction, echo=TRUE-------------------------------------------- formula <- "final_fail ~ ." female_model <- glm(formula = formula, family = binomial(link = "logit"), data = ptg_stud_f_train) male_model <- glm(formula = formula, family = binomial(link = "logit"), data = ptg_stud_m_train) ## ----beta equivalence, echo=TRUE---------------------------------------------- delta_beta <- 0.1 print(beta_equivalence(model_a = female_model, model_b = male_model, delta = delta_beta, alpha = 0.05)) ## ----coef vector equivalence, echo=TRUE--------------------------------------- print(descriptive_equiv(data_a = ptg_stud_f_train, data_b = ptg_stud_m_train, formula = formula, delta = delta_beta, alpha = 0.05)) ## ----beta vectorial delta, echo=TRUE------------------------------------------ set.seed(1) delta_beta_vec <- 0.01 * runif(39) print(beta_equivalence(model_a = female_model, model_b = male_model, delta = delta_beta_vec, alpha = 0.05)) ## ----individual predictive equivalence female test, echo=TRUE----------------- r <- 0.05 print(individual_predictive_equiv(model_a = female_model, model_b = male_model, test_data = ptg_stud_f_test, r = r, alpha = 0.05)) ## ----individual predictive equivalence male test, echo=TRUE------------------- r <- 0.025 print(individual_predictive_equiv(model_a = female_model, model_b = male_model, test_data = ptg_stud_m_test, r = r, alpha = 0.05)) ## ----performance equivalence female test, echo=TRUE--------------------------- testing_data <- ptg_stud_m_test print(performance_equiv(model_a = female_model, model_b = male_model, test_data = testing_data, dv_index = 30, delta_B = 1.1, alpha = 0.05)) print(performance_equiv(model_a = female_model, model_b = male_model, test_data = ptg_stud_f_test, dv_index = 30, delta_B = 1.1, alpha = 0.05))