Model diagnostics for logistic regression

library('tidyverse')
library('faraway')
set.seed(1)

n <- 100
dat <- tibble(x1 = rnorm(n), 
              x2 = rnorm(n),
              x3 = rnorm(n),)

X <- model.matrix(~ ., data = dat)
beta <- rnorm(ncol(X))



dat <- dat |> 
  mutate(mu = X %*% beta,
         p = plogis(mu),
         result = rbinom(n, 1, p))

fitted_model <- glm(result ~ x1 + x2 + x3, data = dat, family = binomial())

plot(fitted_model)

prplot(fitted_model, 1)

prplot(fitted_model, 2)

prplot(fitted_model, 3)