tot <- rep(10, 100)
suc <- rbinom(100, prob = 0.9, size = tot)
df <- data.frame(tot, suc)
df$prop <- suc / tot

mod1 <- glm(cbind(suc, tot - suc) ~ 1,
  family = binomial,
  data = df
)

performance::check_posterior_predictions(mod1)

mod2 <- glm(prop ~ 1,
  family = binomial,
  data = df,
  weights = tot
)

performance::check_posterior_predictions(mod2)

mod3 <- glm(cbind(suc, tot) ~ 1,
  family = binomial,
  data = df
)

performance::check_posterior_predictions(mod3)

tot <- rep(10, 100)
suc <- rbinom(100, prob = 0.9, size = tot)
df <- data.frame(tot, suc)
df$prop <- suc / tot

mod1 <- glm(cbind(suc, tot - suc) ~ 1,
  family = binomial,
  data = df
)

performance::check_posterior_predictions(mod1)
#> Warning: Maximum value of original data is not included in the
#>   replicated data.
#>   Model may not capture the variation of the data.

mod2 <- glm(prop ~ 1,
  family = binomial,
  data = df,
  weights = tot
)

performance::check_posterior_predictions(mod2)

mod3 <- glm(cbind(suc, tot) ~ 1,
  family = binomial,
  data = df
)

performance::check_posterior_predictions(mod3)
#> Warning: Maximum value of original data is not included in the
#>   replicated data.
#>   Model may not capture the variation of the data.

set.seed(123)
tot <- rep(10, 100)
suc <- rbinom(100, prob = 0.9, size = tot)
df <- data.frame(tot, suc)
df$prop <- suc / tot

mod1 <- glm(cbind(suc, tot - suc) ~ 1,
  family = binomial,
  data = df
)

mod2 <- glm(prop ~ 1,
  family = binomial,
  data = df,
  weights = tot
)

mod3 <- glm(cbind(suc, tot) ~ 1,
  family = binomial,
  data = df
)

mod4 <- glm(am ~ 1,
  family = binomial,
  data = mtcars
)

performance::check_predictions(mod1)

performance::check_predictions(mod2)

performance::check_predictions(mod3)

performance::check_predictions(mod4)

set.seed(1)
tot <- rep(10, 100)
suc <- rbinom(100, prob = 0.9, size = tot)
df <- data.frame(tot, suc)
df$prop <- suc / tot

mod1 <- glm(cbind(suc, tot - suc) ~ 1,
  family = binomial,
  data = df
)

mod2 <- glm(prop ~ 1,
  family = binomial,
  data = df,
  weights = tot
)

mod3 <- glm(cbind(suc, tot) ~ 1,
  family = binomial,
  data = df
)

mod4 <- glm(am ~ 1,
  family = binomial,
  data = mtcars
)