broom.helpers (version 1.14.0)

tidy_plus_plus: Tidy a model and compute additional informations

Description

This function will apply sequentially:

• tidy_and_attach()

• tidy_disambiguate_terms()

• tidy_identify_variables()

• tidy_add_contrasts()

• tidy_add_reference_rows()

• tidy_add_pairwise_contrasts()

• tidy_add_estimate_to_reference_rows()

• tidy_add_variable_labels()

• tidy_add_term_labels()

• tidy_add_header_rows()

• tidy_add_n()

• tidy_remove_intercept()

• tidy_select_variables()

• tidy_add_coefficients_type()

• tidy_detach_model()

Usage

tidy_plus_plus(
  model,
  tidy_fun = tidy_with_broom_or_parameters,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  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,
  quiet = FALSE,
  strict = FALSE,
  ...
)

Arguments

model

a model to be attached/tidied

tidy_fun

option to specify a custom tidier function

conf.int

should confidence intervals be computed? (see broom::tidy())

conf.level

level of confidence for confidence intervals (default: 95%)

exponentiate

logical indicating 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.

variable_labels

a named list or a named vector of custom variable labels

term_labels

a named list or a 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 model_list_terms_levels())

disambiguate_terms

should terms be disambiguated with tidy_disambiguate_terms()? (default TRUE)

disambiguate_sep

separator for tidy_disambiguate_terms()

add_reference_rows

should reference rows be added?

no_reference_row

variables (accepts tidyselect notation) for those no reference row should be added, when add_reference_rows = TRUE

add_pairwise_contrasts

apply tidy_add_pairwise_contrasts()?

pairwise_variables

variables to add pairwise contrasts (accepts tidyselect notation)

keep_model_terms

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

pairwise_reverse

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

contrasts_adjust

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

emmeans_args

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

add_estimate_to_reference_rows

should an estimate value be added to reference rows?

add_header_rows

should header rows be added?

show_single_row

variables that should be displayed on a single row (accepts tidyselect notation), when add_header_rows is TRUE

add_n

should the number of observations be added?

intercept

should the intercept(s) be included?

include

variables to include. Accepts tidyselect syntax. Use - to remove a variable. Default is everything(). See also all_continuous(), all_categorical(), all_dichotomous() and all_interaction()

keep_model

should the model be kept as an attribute of the final result?

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

...

other arguments passed to tidy_fun()

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
}
}