Skip to contents

Tidy a model and compute additional informations

Source: R/tidy_plus_plus.R
tidy_plus_plus.Rd

This function will apply sequentially:

Usage

tidy_plus_plus(
  model,
  tidy_fun = tidy_with_broom_or_parameters,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  model_matrix_attr = TRUE,
  variable_labels = NULL,
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  disambiguate_terms = TRUE,
  disambiguate_sep = ".",
  add_reference_rows = TRUE,
  no_reference_row = NULL,
  add_pairwise_contrasts = FALSE,
  pairwise_variables = all_categorical(),
  keep_model_terms = FALSE,
  pairwise_reverse = TRUE,
  contrasts_adjust = NULL,
  emmeans_args = list(),
  add_estimate_to_reference_rows = TRUE,
  add_header_rows = FALSE,
  show_single_row = NULL,
  add_n = TRUE,
  intercept = FALSE,
  include = everything(),
  keep_model = FALSE,
  tidy_post_fun = NULL,
  quiet = FALSE,
  strict = FALSE,
  ...
)

Arguments

model

(a model object, e.g. glm)
A model to be attached/tidied.

tidy_fun

(function)
Option to specify a custom tidier function.

conf.int

(logical)
Should confidence intervals be computed? (see broom::tidy())

conf.level

(numeric)
Level of confidence for confidence intervals (default: 95%).

exponentiate

(logical)
Whether or not to exponentiate the coefficient estimates. This is typical for logistic, Poisson and Cox models, but a bad idea if there is no log or logit link; defaults to FALSE.

model_matrix_attr

(logical)
Whether model frame and model matrix should be added as attributes of model (respectively named "model_frame" and "model_matrix") and passed through.

variable_labels

(formula-list-selector)
A named list or a named vector of custom variable labels.

term_labels

(list or vector)
A named list or a 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 model_list_terms_levels()).

disambiguate_terms

(logical)
Should terms be disambiguated with tidy_disambiguate_terms()? (default TRUE)

disambiguate_sep

(string)
Separator for tidy_disambiguate_terms().

add_reference_rows

(logical)
Should reference rows be added?

no_reference_row

(tidy-select)
Variables for those no reference row should be added, when add_reference_rows = TRUE.

add_pairwise_contrasts

(logical)
Apply tidy_add_pairwise_contrasts()?

pairwise_variables

(tidy-select)
Variables to add pairwise contrasts.

keep_model_terms

(logical)
Keep original model terms for variables where pairwise contrasts are added? (default is FALSE)

pairwise_reverse

(logical)
Determines whether to use "pairwise" (if TRUE) or "revpairwise" (if FALSE), see emmeans::contrast().

contrasts_adjust

(string)
Optional adjustment method when computing contrasts, see emmeans::contrast() (if NULL, use emmeans default).

emmeans_args

(list)
List of additional parameter to pass to emmeans::emmeans() when computing pairwise contrasts.

add_estimate_to_reference_rows

(logical)
Should an estimate value be added to reference rows?

add_header_rows

(logical)
Should header rows be added?

show_single_row

(tidy-select)
Variables that should be displayed on a single row, when add_header_rows is TRUE.

add_n

(logical)
Should the number of observations be added?

intercept

(logical)
Should the intercept(s) be included?

include

(tidy-select)
Variables to include. Default is everything(). See also all_continuous(), all_categorical(), all_dichotomous() and all_interaction().

keep_model

(logical)
Should the model be kept as an attribute of the final result?

tidy_post_fun

(function)
Custom function applied to the results at the end of tidy_plus_plus() (see note)

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.

...

other arguments passed to tidy_fun()

Note

tidy_post_fun is applied to the result at the end of tidy_plus_plus() and receive only one argument (the result of tidy_plus_plus()). However, if needed, the model is still attached to the tibble as an attribute, even if keep_model = FALSE. Therefore, it is possible to use tidy_get_model() within tidy_fun if, for any reason, you need to access the source model.

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_term_labels(), tidy_add_variable_labels(), tidy_attach_model(), tidy_disambiguate_terms(), tidy_identify_variables(), tidy_remove_intercept(), tidy_select_variables()

Examples

if (FALSE) { # interactive()
ex1 <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris) |>
  tidy_plus_plus()
ex1

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(
    Survived = factor(Survived, c("No", "Yes"))
  ) |>
  labelled::set_variable_labels(
    Class = "Passenger's class",
    Sex = "Gender"
  )
ex2 <- glm(
  Survived ~ Class + Age * Sex,
  data = df, weights = df$n,
  family = binomial
) |>
  tidy_plus_plus(
    exponentiate = TRUE,
    add_reference_rows = FALSE,
    categorical_terms_pattern = "{level} / {reference_level}",
    add_n = TRUE
  )
ex2
if (.assert_package("gtsummary", boolean = TRUE)) {
  ex3 <-
    glm(
      response ~ poly(age, 3) + stage + grade * trt,
      na.omit(gtsummary::trial),
      family = binomial,
      contrasts = list(
        stage = contr.treatment(4, base = 3),
        grade = contr.sum
      )
    ) |>
    tidy_plus_plus(
      exponentiate = TRUE,
      variable_labels = c(age = "Age (in years)"),
      add_header_rows = TRUE,
      show_single_row = all_dichotomous(),
      term_labels = c("poly(age, 3)3" = "Cubic age"),
      keep_model = TRUE
    )
  ex3
}
}