Marginal Means with marginaleffects::marginal_means()

Use marginaleffects::marginal_means() to estimate marginal means and return a tibble tidied in a way that it could be used by broom.helpers functions. See marginaleffects::marginal_means()() for a list of supported models.

tidy_marginal_means(x, conf.int = TRUE, conf.level = 0.95, ...)

Arguments

x

a model

conf.int

logical indicating whether or not to include a confidence interval in the tidied output

conf.level

the confidence level to use for the confidence interval

...

additional parameters passed to marginaleffects::marginal_means()

Details

marginaleffects::marginal_means() estimate marginal means: adjusted predictions, averaged across a grid of categorical predictors, holding other numeric predictors at their means. Please refer to the documentation page of marginaleffects::marginal_means(). Marginal means are defined only for categorical variables.

For more information, see vignette("marginal_tidiers", "broom.helpers").

See also

marginaleffects::marginal_means()

Other marginal_tieders: tidy_all_effects(), tidy_avg_comparisons(), tidy_avg_slopes(), tidy_ggpredict(), tidy_marginal_contrasts(), tidy_marginal_predictions(), tidy_margins()

Examples

if (FALSE) { # interactive()
# Average Marginal Means

df <- Titanic %>%
  dplyr::as_tibble() %>%
  tidyr::uncount(n) %>%
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
mod <- glm(
  Survived ~ Class + Age + Sex,
  data = df, family = binomial
)
tidy_marginal_means(mod)
tidy_plus_plus(mod, tidy_fun = tidy_marginal_means)

mod2 <- lm(Petal.Length ~ poly(Petal.Width, 2) + Species, data = iris)
tidy_marginal_means(mod2)
}