tidy_identify_variables {broom.helpers}

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

a tidy tibble

model

the corresponding model, if not attached to x

quiet

logical argument 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:

• "continuous",

• "dichotomous" (categorical variable with 2 levels),

• "categorical" (categorical variable with 3 levels or more),

• "intercept"

• "interaction"

• ⁠"ran_pars⁠ (random-effect parameters for mixed models)

• "ran_vals" (random-effect values for mixed models)

• "unknown" in the rare cases where tidy_identify_variables() will fail to identify the list of variables

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

Titanic %>%
  dplyr::as_tibble() %>%
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes"))) %>%
  glm(Survived ~ Class + Age * Sex, data = ., weights = .$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()