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}",
  relabel_poly = FALSE,
  model = tidy_get_model(x),
  quiet = FALSE,
  strict = FALSE
)

Arguments

x

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

labels

(list or string)
An optional named list or named vector of custom term labels.

interaction_sep

(string)
Separator for interaction terms.

categorical_terms_pattern

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

relabel_poly

Should terms generated with stats::poly() be relabeled?

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.

strict

(logical)
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_group_by(), tidy_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

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

mod <-
  glm(Survived ~ Class * Age * Sex, data = df, weights = df$n, family = binomial)
mod |>
  tidy_and_attach() |>
  tidy_add_term_labels()
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> # A tibble: 16 × 15
#>    term              variable var_label var_class var_type var_nlevels contrasts
#>    <chr>             <chr>    <chr>     <chr>     <chr>          <int> <chr>    
#>  1 (Intercept)       (Interc… (Interce… NA        interce…          NA NA       
#>  2 Class2nd          Class    Passenge… character categor…           4 contr.tr…
#>  3 Class3rd          Class    Passenge… character categor…           4 contr.tr…
#>  4 ClassCrew         Class    Passenge… character categor…           4 contr.tr…
#>  5 AgeChild          Age      Age       character dichoto…           2 contr.tr…
#>  6 SexMale           Sex      Sex       character dichoto…           2 contr.tr…
#>  7 Class2nd:AgeChild Class:A… Passenge… NA        interac…          NA NA       
#>  8 Class3rd:AgeChild Class:A… Passenge… NA        interac…          NA NA       
#>  9 ClassCrew:AgeChi… Class:A… Passenge… NA        interac…          NA NA       
#> 10 Class2nd:SexMale  Class:S… Passenge… NA        interac…          NA NA       
#> 11 Class3rd:SexMale  Class:S… Passenge… NA        interac…          NA NA       
#> 12 ClassCrew:SexMale Class:S… Passenge… NA        interac…          NA NA       
#> 13 AgeChild:SexMale  Age:Sex  Age * Sex NA        interac…          NA NA       
#> 14 Class2nd:AgeChil… Class:A… Passenge… NA        interac…          NA NA       
#> 15 Class3rd:AgeChil… Class:A… Passenge… NA        interac…          NA NA       
#> 16 ClassCrew:AgeChi… Class:A… Passenge… NA        interac…          NA NA       
#> # ℹ 8 more variables: contrasts_type <chr>, label <chr>, estimate <dbl>,
#> #   std.error <dbl>, statistic <dbl>, p.value <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>
mod |>
  tidy_and_attach() |>
  tidy_add_term_labels(
    interaction_sep = " x ",
    categorical_terms_pattern = "{level} / {reference_level}"
  )
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
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#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> Warning: glm.fit: algorithm did not converge
#> Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
#> # A tibble: 16 × 15
#>    term              variable var_label var_class var_type var_nlevels contrasts
#>    <chr>             <chr>    <chr>     <chr>     <chr>          <int> <chr>    
#>  1 (Intercept)       (Interc… (Interce… NA        interce…          NA NA       
#>  2 Class2nd          Class    Passenge… character categor…           4 contr.tr…
#>  3 Class3rd          Class    Passenge… character categor…           4 contr.tr…
#>  4 ClassCrew         Class    Passenge… character categor…           4 contr.tr…
#>  5 AgeChild          Age      Age       character dichoto…           2 contr.tr…
#>  6 SexMale           Sex      Sex       character dichoto…           2 contr.tr…
#>  7 Class2nd:AgeChild Class:A… Passenge… NA        interac…          NA NA       
#>  8 Class3rd:AgeChild Class:A… Passenge… NA        interac…          NA NA       
#>  9 ClassCrew:AgeChi… Class:A… Passenge… NA        interac…          NA NA       
#> 10 Class2nd:SexMale  Class:S… Passenge… NA        interac…          NA NA       
#> 11 Class3rd:SexMale  Class:S… Passenge… NA        interac…          NA NA       
#> 12 ClassCrew:SexMale Class:S… Passenge… NA        interac…          NA NA       
#> 13 AgeChild:SexMale  Age:Sex  Age * Sex NA        interac…          NA NA       
#> 14 Class2nd:AgeChil… Class:A… Passenge… NA        interac…          NA NA       
#> 15 Class3rd:AgeChil… Class:A… Passenge… NA        interac…          NA NA       
#> 16 ClassCrew:AgeChi… Class:A… Passenge… NA        interac…          NA NA       
#> # ℹ 8 more variables: contrasts_type <chr>, label <chr>, estimate <dbl>,
#> #   std.error <dbl>, statistic <dbl>, p.value <dbl>, conf.low <dbl>,
#> #   conf.high <dbl>
# }