tidy_identify_variables {broom.helpers}R Documentation

Identify the variable corresponding to each model coefficient

Description

tidy_identify_variables() will add to the tidy tibble three additional columns: variable, var_class, var_type and var_nlevels.

Usage

tidy_identify_variables(x, model = tidy_get_model(x), quiet = FALSE)

Arguments

x

(data.frame)
A tidy tibble as produced by ⁠tidy_*()⁠ functions.

model

(a model object, e.g. glm)
The corresponding model, if not attached to x.

quiet

(logical)
Whether broom.helpers should not return a message when requested output cannot be generated. Default is FALSE.

Details

It will also identify interaction terms and intercept(s).

var_type could be:

For dichotomous and categorical variables, var_nlevels corresponds to the number of original levels in the corresponding variables.

For fixest models, a new column instrumental is added to indicate instrumental variables.

See Also

model_identify_variables()

Other tidy_helpers: tidy_add_coefficients_type(), tidy_add_contrasts(), tidy_add_estimate_to_reference_rows(), tidy_add_header_rows(), tidy_add_n(), tidy_add_pairwise_contrasts(), tidy_add_reference_rows(), tidy_add_term_labels(), tidy_add_variable_labels(), tidy_attach_model(), tidy_disambiguate_terms(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
glm(
  Survived ~ Class + Age * Sex,
  data = df,
  weights = df$n,
  family = binomial
) |>
  tidy_and_attach() |>
  tidy_identify_variables()

lm(
  Sepal.Length ~ poly(Sepal.Width, 2) + Species,
  data = iris,
  contrasts = list(Species = contr.sum)
) |>
  tidy_and_attach(conf.int = TRUE) |>
  tidy_identify_variables()

[Package broom.helpers version 1.18.0 Index]