Add an estimate value to references rows for categorical variables

For categorical variables with a treatment contrast (stats::contr.treatment()) or a SAS contrast (stats::contr.SAS()), will add an estimate equal to 0 (or 1 if exponentiate = TRUE) to the reference row.

Usage

tidy_add_estimate_to_reference_rows(
  x,
  exponentiate = attr(x, "exponentiate"),
  conf.level = attr(x, "conf.level"),
  model = tidy_get_model(x),
  quiet = FALSE
)

Arguments

x

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

exponentiate

(logical)
Whether or not to exponentiate the coefficient estimates. It should be consistent with the original call to broom::tidy()

conf.level

(numeric)
Confidence level, by default use the value indicated previously in tidy_and_attach(), used only for sum contrasts.

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

For categorical variables with a sum contrast (stats::contr.sum()), the estimate value of the reference row will be equal to the sum of all other coefficients multiplied by -1 (eventually exponentiated if exponentiate = TRUE), and obtained with emmeans::emmeans(). The emmeans package should therefore be installed. For sum contrasts, the model coefficient corresponds to the difference of each level with the grand mean. For sum contrasts, confidence intervals and p-values will also be computed and added to the reference rows.

For other variables, no change will be made.

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

See also

Other tidy_helpers: tidy_add_coefficients_type(), tidy_add_contrasts(), 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_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

if (FALSE) { # interactive()
if (.assert_package("gtsummary", boolean = TRUE) && .assert_package("emmeans", boolean = TRUE)) {
  df <- Titanic |>
    dplyr::as_tibble() |>
    dplyr::mutate(dplyr::across(where(is.character), factor))

  glm(
    Survived ~ Class + Age + Sex,
    data = df, weights = df$n, family = binomial,
    contrasts = list(Age = contr.sum, Class = "contr.SAS")
  ) |>
    tidy_and_attach(exponentiate = TRUE) |>
    tidy_add_reference_rows() |>
    tidy_add_estimate_to_reference_rows()

  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() |>
    tidy_add_estimate_to_reference_rows()
}
}