Tidy a model and compute additional informations

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,
  instrumental_suffix = " (instrumental)",
  term_labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  relabel_poly = FALSE,
  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(),
  group_by = auto_group_by(),
  group_labels = NULL,
  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.
instrumental_suffix: (string)
Suffix added to variable labels for instrumental variables (fixest models). NULL to add nothing.
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()).
relabel_poly: Should terms generated with stats::poly() be relabeled?
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().
group_by: (tidy-select)
One or several variables to group by. Default is auto_group_by(). Use NULL to force ungrouping.
group_labels: (string)
An optional named vector of custom term labels.
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.

Examples

# \donttest{
ex1 <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris) |>
  tidy_plus_plus()
ex1
#> # A tibble: 4 × 17
#>   term              variable  var_label var_class var_type var_nlevels contrasts
#>   <chr>             <chr>     <chr>     <chr>     <chr>          <int> <chr>    
#> 1 Sepal.Width       Sepal.Wi… Sepal.Wi… numeric   continu…          NA NA       
#> 2 Speciessetosa     Species   Species   factor    categor…           3 contr.tr…
#> 3 Speciesversicolor Species   Species   factor    categor…           3 contr.tr…
#> 4 Speciesvirginica  Species   Species   factor    categor…           3 contr.tr…
#> # ℹ 10 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> #   n_obs <dbl>, estimate <dbl>, std.error <dbl>, statistic <dbl>,
#> #   p.value <dbl>, conf.low <dbl>, conf.high <dbl>

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
#> # A tibble: 6 × 17
#>   term             variable var_label   var_class var_type var_nlevels contrasts
#>   <chr>            <chr>    <chr>       <chr>     <chr>          <int> <chr>    
#> 1 Class2nd         Class    Passenger'… character categor…           4 contr.tr…
#> 2 Class3rd         Class    Passenger'… character categor…           4 contr.tr…
#> 3 ClassCrew        Class    Passenger'… character categor…           4 contr.tr…
#> 4 AgeChild         Age      Age         character dichoto…           2 contr.tr…
#> 5 SexMale          Sex      Gender      character dichoto…           2 contr.tr…
#> 6 AgeChild:SexMale Age:Sex  Age * Gend… NA        interac…          NA NA       
#> # ℹ 10 more variables: contrasts_type <chr>, label <chr>, n_obs <dbl>,
#> #   n_event <dbl>, estimate <dbl>, std.error <dbl>, statistic <dbl>,
#> #   p.value <dbl>, conf.low <dbl>, conf.high <dbl>
# }
# \donttest{
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
#> # A tibble: 17 × 19
#>    term   variable var_label var_class var_type var_nlevels header_row contrasts
#>    <chr>  <chr>    <chr>     <chr>     <chr>          <int> <lgl>      <chr>    
#>  1 NA     age      Age (in … nmatrix.3 continu…          NA TRUE       NA       
#>  2 poly(… age      Age (in … nmatrix.3 continu…          NA FALSE      NA       
#>  3 poly(… age      Age (in … nmatrix.3 continu…          NA FALSE      NA       
#>  4 poly(… age      Age (in … nmatrix.3 continu…          NA FALSE      NA       
#>  5 NA     stage    T Stage   factor    categor…           4 TRUE       contr.tr…
#>  6 stage1 stage    T Stage   factor    categor…           4 FALSE      contr.tr…
#>  7 stage2 stage    T Stage   factor    categor…           4 FALSE      contr.tr…
#>  8 stage3 stage    T Stage   factor    categor…           4 FALSE      contr.tr…
#>  9 stage4 stage    T Stage   factor    categor…           4 FALSE      contr.tr…
#> 10 NA     grade    Grade     factor    categor…           3 TRUE       contr.sum
#> 11 grade1 grade    Grade     factor    categor…           3 FALSE      contr.sum
#> 12 grade2 grade    Grade     factor    categor…           3 FALSE      contr.sum
#> 13 grade3 grade    Grade     factor    categor…           3 FALSE      contr.sum
#> 14 trtDr… trt      Chemothe… character dichoto…           2 NA         contr.tr…
#> 15 NA     grade:t… Grade * … NA        interac…          NA TRUE       NA       
#> 16 grade… grade:t… Grade * … NA        interac…          NA FALSE      NA       
#> 17 grade… grade:t… Grade * … NA        interac…          NA FALSE      NA       
#> # ℹ 11 more variables: contrasts_type <chr>, reference_row <lgl>, label <chr>,
#> #   n_obs <dbl>, n_event <dbl>, estimate <dbl>, std.error <dbl>,
#> #   statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>
# }

Usage

Arguments

Note

See also

Examples