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Add references rows for categorical variables

Source: R/tidy_add_reference_rows.R
tidy_add_reference_rows.Rd

For categorical variables with a treatment contrast (stats::contr.treatment()), a SAS contrast (stats::contr.SAS()) a sum contrast (stats::contr.sum()), or successive differences contrast (MASS::contr.sdif()) add a reference row.

Usage

tidy_add_reference_rows(
  x,
  no_reference_row = NULL,
  model = tidy_get_model(x),
  quiet = FALSE
)

Arguments

x

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

no_reference_row

(tidy-select)
Variables for those no reference row should be added. See also all_categorical() and all_dichotomous().

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

The added reference_row column will be equal to:

  • TRUE for a reference row;

  • FALSE for a normal row of a variable with a reference row;

  • NA for variables without a reference row.

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

tidy_add_reference_rows() will not populate the label of the reference term. It is therefore better to apply tidy_add_term_labels() after tidy_add_reference_rows() rather than before. Similarly, it is better to apply tidy_add_reference_rows() before tidy_add_n().

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_term_labels(), 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")))

  res <-
    glm(
      Survived ~ Class + Age + Sex,
      data = df, weights = df$n, family = binomial,
      contrasts = list(Age = contr.sum, Class = "contr.SAS")
    ) |>
    tidy_and_attach()
  res |> tidy_add_reference_rows()
#> # A tibble: 9 × 14
#>   term        variable   var_class var_type var_nlevels contrasts contrasts_type
#>   <chr>       <chr>      <chr>     <chr>          <int> <chr>     <chr>         
#> 1 (Intercept) (Intercep… NA        interce…          NA NA        NA            
#> 2 Class1st    Class      character categor…           4 contr.SAS treatment     
#> 3 Class2nd    Class      character categor…           4 contr.SAS treatment     
#> 4 Class3rd    Class      character categor…           4 contr.SAS treatment     
#> 5 ClassCrew   Class      character categor…           4 contr.SAS treatment     
#> 6 Age1        Age        character dichoto…           2 contr.sum sum           
#> 7 Age2        Age        character dichoto…           2 contr.sum sum           
#> 8 SexFemale   Sex        character dichoto…           2 contr.tr… treatment     
#> 9 SexMale     Sex        character dichoto…           2 contr.tr… treatment     
#> # ℹ 7 more variables: reference_row <lgl>, estimate <dbl>, std.error <dbl>,
#> #   statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>
  res |> tidy_add_reference_rows(no_reference_row = all_dichotomous())
#> # A tibble: 7 × 14
#>   term        variable   var_class var_type var_nlevels contrasts contrasts_type
#>   <chr>       <chr>      <chr>     <chr>          <int> <chr>     <chr>         
#> 1 (Intercept) (Intercep… NA        interce…          NA NA        NA            
#> 2 Class1st    Class      character categor…           4 contr.SAS treatment     
#> 3 Class2nd    Class      character categor…           4 contr.SAS treatment     
#> 4 Class3rd    Class      character categor…           4 contr.SAS treatment     
#> 5 ClassCrew   Class      character categor…           4 contr.SAS treatment     
#> 6 Age1        Age        character dichoto…           2 contr.sum sum           
#> 7 SexMale     Sex        character dichoto…           2 contr.tr… treatment     
#> # ℹ 7 more variables: reference_row <lgl>, estimate <dbl>, std.error <dbl>,
#> #   statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>
  res |> tidy_add_reference_rows(no_reference_row = "Class")
#> # A tibble: 8 × 14
#>   term        variable   var_class var_type var_nlevels contrasts contrasts_type
#>   <chr>       <chr>      <chr>     <chr>          <int> <chr>     <chr>         
#> 1 (Intercept) (Intercep… NA        interce…          NA NA        NA            
#> 2 Class1st    Class      character categor…           4 contr.SAS treatment     
#> 3 Class2nd    Class      character categor…           4 contr.SAS treatment     
#> 4 Class3rd    Class      character categor…           4 contr.SAS treatment     
#> 5 Age1        Age        character dichoto…           2 contr.sum sum           
#> 6 Age2        Age        character dichoto…           2 contr.sum sum           
#> 7 SexFemale   Sex        character dichoto…           2 contr.tr… treatment     
#> 8 SexMale     Sex        character dichoto…           2 contr.tr… treatment     
#> # ℹ 7 more variables: reference_row <lgl>, estimate <dbl>, std.error <dbl>,
#> #   statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>

  glm(
    response ~ stage + grade * trt,
    gtsummary::trial,
    family = binomial,
    contrasts = list(
      stage = contr.treatment(4, base = 3),
      grade = contr.treatment(3, base = 2),
      trt = contr.treatment(2, base = 2)
    )
  ) |>
    tidy_and_attach() |>
    tidy_add_reference_rows()
#> # A tibble: 12 × 14
#>    term        variable  var_class var_type var_nlevels contrasts contrasts_type
#>    <chr>       <chr>     <chr>     <chr>          <int> <chr>     <chr>         
#>  1 (Intercept) (Interce… NA        interce…          NA NA        NA            
#>  2 stage1      stage     factor    categor…           4 contr.tr… treatment     
#>  3 stage2      stage     factor    categor…           4 contr.tr… treatment     
#>  4 stage3      stage     factor    categor…           4 contr.tr… treatment     
#>  5 stage4      stage     factor    categor…           4 contr.tr… treatment     
#>  6 grade1      grade     factor    categor…           3 contr.tr… treatment     
#>  7 grade2      grade     factor    categor…           3 contr.tr… treatment     
#>  8 grade3      grade     factor    categor…           3 contr.tr… treatment     
#>  9 trt1        trt       character dichoto…           2 contr.SAS treatment     
#> 10 trt2        trt       character dichoto…           2 contr.SAS treatment     
#> 11 grade1:trt1 grade:trt NA        interac…          NA NA        NA            
#> 12 grade3:trt1 grade:trt NA        interac…          NA NA        NA            
#> # ℹ 7 more variables: reference_row <lgl>, estimate <dbl>, std.error <dbl>,
#> #   statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>
# }