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.
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 (between0ans1).- ...
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
Other marginal_tieders:
tidy_all_effects(),
tidy_avg_comparisons(),
tidy_avg_slopes(),
tidy_marginal_contrasts(),
tidy_marginal_predictions(),
tidy_margins()
Examples
# \donttest{
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)
#> Some of the focal terms are of type `character`. This may lead to
#> unexpected results. It is recommended to convert these variables to
#> factors before fitting the model.
#> The following variables are of type character: `Class`
#> Some of the focal terms are of type `character`. This may lead to
#> unexpected results. It is recommended to convert these variables to
#> factors before fitting the model.
#> The following variables are of type character: `Age`
#> Some of the focal terms are of type `character`. This may lead to
#> unexpected results. It is recommended to convert these variables to
#> factors before fitting the model.
#> The following variables are of type character: `Sex`
#> # A tibble: 8 × 6
#> variable term estimate std.error conf.low conf.high
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Class 3rd 0.104 0.127 0.0830 0.130
#> 2 Class 1st 0.407 0.136 0.345 0.473
#> 3 Class 2nd 0.199 0.155 0.155 0.252
#> 4 Class Crew 0.225 0.0805 0.199 0.254
#> 5 Age Child 0.457 0.257 0.337 0.582
#> 6 Age Adult 0.225 0.0805 0.199 0.254
#> 7 Sex Male 0.225 0.0805 0.199 0.254
#> 8 Sex Female 0.766 0.159 0.706 0.817
tidy_plus_plus(mod, tidy_fun = tidy_ggpredict)
#> Some of the focal terms are of type `character`. This may lead to
#> unexpected results. It is recommended to convert these variables to
#> factors before fitting the model.
#> The following variables are of type character: `Class`
#> Some of the focal terms are of type `character`. This may lead to
#> unexpected results. It is recommended to convert these variables to
#> factors before fitting the model.
#> The following variables are of type character: `Age`
#> Some of the focal terms are of type `character`. This may lead to
#> unexpected results. It is recommended to convert these variables to
#> factors before fitting the model.
#> The following variables are of type character: `Sex`
#> # A tibble: 8 × 17
#> term variable var_label var_class var_type var_nlevels contrasts
#> <chr> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 3rd Class Class character categorical 4 contr.treatment
#> 2 1st Class Class character categorical 4 contr.treatment
#> 3 2nd Class Class character categorical 4 contr.treatment
#> 4 Crew Class Class character categorical 4 contr.treatment
#> 5 Child Age Age character dichotomous 2 contr.treatment
#> 6 Adult Age Age character dichotomous 2 contr.treatment
#> 7 Male Sex Sex character dichotomous 2 contr.treatment
#> 8 Female Sex Sex character dichotomous 2 contr.treatment
#> # ℹ 10 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> # n_obs <dbl>, n_event <dbl>, estimate <dbl>, std.error <dbl>,
#> # conf.low <dbl>, conf.high <dbl>, label_attr <chr>
# }