Marginal Predictions with ggeffects::ggpredict()

Use ggeffects::ggpredict() to estimate marginal predictions and return a tibble tidied in a way that it could be used by broom.helpers functions. See https://strengejacke.github.io/ggeffects/ for a list of supported models.

Usage

tidy_ggpredict(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 ggeffects::ggpredict().

Details

By default, ggeffects::ggpredict() estimate marginal predictions at the observed mean of continuous variables and at the first modality of categorical variables (regardless of the type of contrasts used in the model).

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

Note

By default, ggeffects::ggpredict() estimates marginal predictions for each individual variable, regardless of eventual interactions.

See also

ggeffects::ggpredict()

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

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_ggpredict(mod)
tidy_plus_plus(mod, tidy_fun = tidy_ggpredict)
}