View source: R/marginal_tidiers.R
tidy_avg_comparisons | R Documentation |
marginaleffects::avg_comparisons()
Use
marginaleffects::avg_comparisons()
to estimate marginal contrasts and
return a tibble tidied in a way that it could be used by broom.helpers
functions. See marginaleffects::avg_comparisons()
for a list of supported
models.
tidy_avg_comparisons(x, conf.int = TRUE, conf.level = 0.95, ...)
x |
(a model object, e.g. |
conf.int |
( |
conf.level |
( |
... |
Additional parameters passed to
|
By default, marginaleffects::avg_comparisons()
estimate average marginal
contrasts: a contrast is computed for each observed value in the original
dataset (counterfactual approach) before being averaged.
Marginal Contrasts at the Mean could be computed by specifying
newdata = "mean"
. The variables
argument can be used to select the
contrasts to be computed. Please refer to the documentation page of
marginaleffects::avg_comparisons()
.
See also tidy_marginal_contrasts()
for taking into account interactions.
For more information, see vignette("marginal_tidiers", "broom.helpers")
.
marginaleffects::avg_comparisons()
Other marginal_tieders:
tidy_all_effects()
,
tidy_avg_slopes()
,
tidy_ggpredict()
,
tidy_marginal_contrasts()
,
tidy_marginal_means()
,
tidy_marginal_predictions()
,
tidy_margins()
# Average Marginal Contrasts
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_avg_comparisons(mod)
tidy_plus_plus(mod, tidy_fun = tidy_avg_comparisons)
mod2 <- lm(Petal.Length ~ poly(Petal.Width, 2) + Species, data = iris)
tidy_avg_comparisons(mod2)
# Custumizing the type of contrasts
tidy_avg_comparisons(
mod2,
variables = list(Petal.Width = 2, Species = "pairwise")
)
# Marginal Contrasts at the Mean
tidy_avg_comparisons(mod, newdata = "mean")
tidy_plus_plus(mod, tidy_fun = tidy_avg_comparisons, newdata = "mean")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.