## ----eval=FALSE--------------------------------------------------------------- # library(tidyverse) # library(moderndive) # library(skimr) # library(ISLR) ## ----echo=FALSE, message=FALSE, purl=TRUE------------------------------------- # The code presented to the reader in the chunk above is different than the code # in this chunk that is actually run to build the book. In particular we do not # load the skimr package. # # This is because skimr v1.0.6 which we used for the book causes all # kable() code to break for the remaining chapters in the book. v2 might # fix these issues: # https://github.com/moderndive/ModernDive_book/issues/271 # As a workaround for v1 of ModernDive, all skimr::skim() output in this chapter # has been hard coded. library(tidyverse) library(moderndive) # library(skimr) library(gapminder) ## ----------------------------------------------------------------------------- evals_ch6 <- evals %>% select(ID, score, age, gender) ## ----------------------------------------------------------------------------- glimpse(evals_ch6) ## ----eval=FALSE--------------------------------------------------------------- # evals_ch6 %>% sample_n(size = 5) ## ----eval=FALSE--------------------------------------------------------------- # evals_ch6 %>% select(score, age, gender) %>% skim() ## ----------------------------------------------------------------------------- evals_ch6 %>% get_correlation(formula = score ~ age) ## ----eval=FALSE--------------------------------------------------------------- # ggplot(evals_ch6, aes(x = age, y = score, color = gender)) + # geom_point() + # labs(x = "Age", y = "Teaching Score", color = "Gender") + # geom_smooth(method = "lm", se = FALSE) ## ----eval=FALSE--------------------------------------------------------------- # # Fit regression model: # score_model_interaction <- lm(score ~ age * gender, data = evals_ch6) # # # Get regression table: # get_regression_table(score_model_interaction) ## ----eval=FALSE--------------------------------------------------------------- # ggplot(evals_ch6, aes(x = age, y = score, color = gender)) + # geom_point() + # labs(x = "Age", y = "Teaching Score", color = "Gender") + # geom_parallel_slopes(se = FALSE) ## ----eval=FALSE--------------------------------------------------------------- # # Fit regression model: # score_model_parallel_slopes <- lm(score ~ age + gender, data = evals_ch6) # # Get regression table: # get_regression_table(score_model_parallel_slopes) ## ----echo=FALSE--------------------------------------------------------------- age_coef <- get_regression_table(score_model_parallel_slopes) %>% filter(term == "age") %>% pull(estimate) ## ----eval=FALSE--------------------------------------------------------------- # regression_points <- get_regression_points(score_model_interaction) # regression_points ## ----message=FALSE------------------------------------------------------------ library(ISLR) credit_ch6 <- Credit %>% as_tibble() %>% select(ID, debt = Balance, credit_limit = Limit, income = Income, credit_rating = Rating, age = Age) ## ----------------------------------------------------------------------------- glimpse(credit_ch6) ## ----eval=FALSE--------------------------------------------------------------- # credit_ch6 %>% sample_n(size = 5) ## ----eval=FALSE--------------------------------------------------------------- # credit_ch6 %>% select(debt, credit_limit, income) %>% skim() ## ----eval=FALSE--------------------------------------------------------------- # credit_ch6 %>% get_correlation(debt ~ credit_limit) # credit_ch6 %>% get_correlation(debt ~ income) ## ----eval=FALSE--------------------------------------------------------------- # credit_ch6 %>% # select(debt, credit_limit, income) %>% # cor() ## ----eval=FALSE--------------------------------------------------------------- # ggplot(credit_ch6, aes(x = credit_limit, y = debt)) + # geom_point() + # labs(x = "Credit limit (in $)", y = "Credit card debt (in $)", # title = "Debt and credit limit") + # geom_smooth(method = "lm", se = FALSE) # # ggplot(credit_ch6, aes(x = income, y = debt)) + # geom_point() + # labs(x = "Income (in $1000)", y = "Credit card debt (in $)", # title = "Debt and income") + # geom_smooth(method = "lm", se = FALSE) ## ----eval=FALSE--------------------------------------------------------------- # # Fit regression model: # debt_model <- lm(debt ~ credit_limit + income, data = credit_ch6) # # Get regression table: # get_regression_table(debt_model) ## ----eval=FALSE--------------------------------------------------------------- # get_regression_points(debt_model) ## ----eval=FALSE--------------------------------------------------------------- # # Interaction model # ggplot(MA_schools, # aes(x = perc_disadvan, y = average_sat_math, color = size)) + # geom_point(alpha = 0.25) + # geom_smooth(method = "lm", se = FALSE) + # labs(x = "Percent economically disadvantaged", y = "Math SAT Score", # color = "School size", title = "Interaction model") ## ----eval=FALSE--------------------------------------------------------------- # # Parallel slopes model # ggplot(MA_schools, # aes(x = perc_disadvan, y = average_sat_math, color = size)) + # geom_point(alpha = 0.25) + # geom_parallel_slopes(se = FALSE) + # labs(x = "Percent economically disadvantaged", y = "Math SAT Score", # color = "School size", title = "Parallel slopes model") ## ----eval=FALSE--------------------------------------------------------------- # model_2_interaction <- lm(average_sat_math ~ perc_disadvan * size, # data = MA_schools) # get_regression_table(model_2_interaction) ## ----eval=FALSE--------------------------------------------------------------- # model_2_parallel_slopes <- lm(average_sat_math ~ perc_disadvan + size, # data = MA_schools) # get_regression_table(model_2_parallel_slopes) ## ----------------------------------------------------------------------------- get_regression_points(model_2_interaction) ## ----------------------------------------------------------------------------- get_regression_points(model_2_interaction) %>% summarize(var_y = var(average_sat_math), var_y_hat = var(average_sat_math_hat), var_residual = var(residual)) ## ----model2-r-squared, echo=FALSE--------------------------------------------- variances_interaction <- get_regression_points(model_2_interaction) %>% summarize(var_y = var(average_sat_math), var_y_hat = var(average_sat_math_hat), var_residual = var(residual)) %>% mutate(model = "Interaction", r_squared = var_y_hat/var_y) variances_parallel_slopes <- get_regression_points(model_2_parallel_slopes) %>% summarize(var_y = var(average_sat_math), var_y_hat = var(average_sat_math_hat), var_residual = var(residual)) %>% mutate(model = "Parallel slopes", r_squared = var_y_hat/var_y) bind_rows( variances_interaction, variances_parallel_slopes ) %>% select(model, var_y, var_y_hat, var_residual, r_squared) %>% kbl( digits = 3, caption = "Comparing variances from interaction and parallel slopes models for MA school data", booktabs = TRUE, linesep = "" ) %>% kable_styling(font_size = ifelse(knitr:::is_latex_output(), 10, 16), latex_options = c("hold_position")) ## ----model1-r-squared, echo=FALSE--------------------------------------------- variances_interaction <- get_regression_points(score_model_interaction) %>% summarize(var_y = var(score), var_y_hat = var(score_hat), var_residual = var(residual)) %>% mutate(model = "Interaction", r_squared = var_y_hat/var_y) variances_parallel_slopes <- get_regression_points(score_model_parallel_slopes) %>% summarize(var_y = var(score), var_y_hat = var(score_hat), var_residual = var(residual)) %>% mutate(model = "Parallel slopes", r_squared = var_y_hat/var_y) bind_rows( variances_interaction, variances_parallel_slopes ) %>% select(model, var_y, var_y_hat, var_residual, r_squared) %>% kbl( digits = 3, caption = "Comparing variances from interaction and parallel slopes models for UT Austin data", booktabs = TRUE, linesep = "" ) %>% kable_styling(font_size = ifelse(knitr:::is_latex_output(), 10, 16), latex_options = c("hold_position")) ## ----------------------------------------------------------------------------- # R-squared for interaction model: get_regression_summaries(model_2_interaction) # R-squared for parallel slopes model: get_regression_summaries(model_2_parallel_slopes) ## ----eval=FALSE--------------------------------------------------------------- # credit_ch6 %>% select(debt, income) %>% # mutate(income = income * 1000) %>% # cor()