Average Marginal Effects with margins::margins()

Usage

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

Arguments

x

(a model object, e.g. glm)
A model to be tidied.

conf.int

(logical)
Whether or not to include a confidence interval in the tidied output.

conf.level

(numeric)
The confidence level to use for the confidence interval (between 0 ans 1).

...

Additional parameters passed to margins::margins().

Details

The margins package is no longer under active development and may be removed from CRAN sooner or later. It is advised to use the marginaleffects package instead, offering more functionalities. You could have a look at the article dedicated to marginal estimates with broom.helpers. tidy_avg_slopes() could be used as an alternative.

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

By default, margins::margins() estimate average marginal effects (AME): an effect is computed for each observed value in the original dataset before being averaged.

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

Note

When applying margins::margins(), custom contrasts are ignored. Treatment contrasts (stats::contr.treatment()) are applied to all categorical variables. Interactions are also ignored.

See also

margins::margins()

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

Examples

if (FALSE) { # interactive()
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_margins(mod)
tidy_plus_plus(mod, tidy_fun = tidy_margins)
}