Comments (2)
I'm not sure, the first three models are no Bernoulli trials, only the 4th is, which is correctly detected:
tot <- rep(10, 100)
suc <- rbinom(100, prob = 0.9, size = tot)
df <- data.frame(tot, suc)
df$prop <- suc / tot
mod1 <- glm(cbind(suc, tot - suc) ~ 1,
family = binomial,
data = df
)
mod2 <- glm(prop ~ 1,
family = binomial,
data = df,
weights = tot
)
mod3 <- glm(cbind(suc, tot) ~ 1,
family = binomial,
data = df
)
mod4 <- glm(am ~ 1,
family = binomial,
data = mtcars
)
insight::model_info(mod1)$is_binomial
#> [1] TRUE
insight::model_info(mod1)$is_bernoulli
#> [1] FALSE
insight::model_info(mod2)$is_binomial
#> [1] TRUE
insight::model_info(mod2)$is_bernoulli
#> [1] FALSE
insight::model_info(mod3)$is_binomial
#> [1] TRUE
insight::model_info(mod3)$is_bernoulli
#> [1] FALSE
insight::model_info(mod4)$is_binomial
#> [1] TRUE
insight::model_info(mod4)$is_bernoulli
#> [1] TRUE
Created on 2023-10-28 with reprex v2.0.2
from insight.
I think you may have been lucky with your seed. mod2$is_bernoulli
is TRUE for me
set.seed(1)
tot <- rep(10, 100)
suc <- rbinom(100, prob = 0.9, size = tot)
df <- data.frame(tot, suc)
df$prop <- suc / tot
mod2 <- glm(prop ~ 1,
family = binomial,
data = df,
weights = tot
)
df$prop
#> [1] 1.0 0.9 0.9 0.8 1.0 0.8 0.7 0.9 0.9 1.0 1.0 1.0 0.9 0.9 0.8 0.9 0.9 0.6
#> [19] 0.9 0.8 0.7 1.0 0.9 1.0 1.0 0.9 1.0 0.9 0.8 1.0 0.9 0.9 0.9 1.0 0.8 0.9
#> [37] 0.8 1.0 0.9 0.9 0.8 0.9 0.8 0.9 0.9 0.8 1.0 0.9 0.9 0.9 0.9 0.8 0.9 1.0
#> [55] 1.0 1.0 1.0 0.9 0.9 0.9 0.8 1.0 0.9 1.0 0.9 1.0 0.9 0.8 1.0 0.8 1.0 0.8
#> [73] 1.0 1.0 0.9 0.8 0.8 0.9 0.8 0.7 0.9 0.9 0.9 1.0 0.8 1.0 0.9 1.0 1.0 1.0
#> [91] 1.0 1.0 0.9 0.8 0.8 0.8 0.9 0.9 0.8 0.9
insight::model_info(mod2)$is_binomial
#> [1] TRUE
insight::model_info(mod2)$is_bernoulli
#> [1] TRUE
Created on 2023-10-28 with reprex v2.0.2
from insight.
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