Add term labels

Will add term labels in a label column, based on:

1. labels provided in labels argument if provided;

2. factor levels for categorical variables coded with treatment, SAS or sum contrasts (the label could be customized with categorical_terms_pattern argument);

3. variable labels when there is only one term per variable;

4. term name otherwise.

tidy_add_term_labels(
  x,
  labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  model = tidy_get_model(x),
  quiet = FALSE,
  strict = FALSE
)

Arguments

x

a tidy tibble

labels

an optional named list or named vector of custom term labels

interaction_sep

separator for interaction terms

categorical_terms_pattern

a glue pattern for labels of categorical terms with treatment or sum contrasts (see examples and model_list_terms_levels())

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

strict

logical argument whether broom.helpers should return an error when requested output cannot be generated. Default is FALSE

Details

If the variable_label column is not yet available in x, tidy_add_variable_labels() will be automatically applied. If the contrasts column is not yet available in x, tidy_add_contrasts() will be automatically applied.

It is possible to pass a custom label for any term in labels, including interaction terms.

See also

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_variable_labels(), tidy_attach_model(), tidy_disambiguate_terms(), tidy_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

if (FALSE) { # interactive()
df <- Titanic %>%
  dplyr::as_tibble() %>%
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes"))) %>%
  labelled::set_variable_labels(
    Class = "Passenger's class",
    Sex = "Sex"
  )

mod <- df %>%
  glm(Survived ~ Class * Age * Sex, data = ., weights = .$n, family = binomial)
mod %>%
  tidy_and_attach() %>%
  tidy_add_term_labels()
mod %>%
  tidy_and_attach() %>%
  tidy_add_term_labels(
    interaction_sep = " x ",
    categorical_terms_pattern = "{level} / {reference_level}"
  )
}