This function will apply sequentially:
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,
...
)
a model to be attached/tidied
option to specify a custom tidier function
should confidence intervals be computed? (see broom::tidy()
)
level of confidence for confidence intervals (default: 95%)
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
.
a named list or a named vector of custom variable labels
a named list or a named vector of custom term labels
separator for interaction terms
a glue pattern for
labels of categorical terms with treatment or sum contrasts
(see model_list_terms_levels()
)
should terms be disambiguated with
tidy_disambiguate_terms()
? (default TRUE
)
separator for tidy_disambiguate_terms()
should reference rows be added?
variables (accepts tidyselect notation)
for those no reference row should be added, when add_reference_rows = TRUE
variables to add pairwise contrasts (accepts tidyselect notation)
keep original model terms for variables where
pairwise contrasts are added? (default is FALSE
)
determines whether to use "pairwise"
(if TRUE
)
or "revpairwise"
(if FALSE
), see emmeans::contrast()
optional adjustment method when computing contrasts,
see emmeans::contrast()
(if NULL
, use emmeans
default)
list of additional parameter to pass to
emmeans::emmeans()
when computing pairwise contrasts
should an estimate value be added to reference rows?
should header rows be added?
variables that should be displayed
on a single row (accepts tidyselect notation), when
add_header_rows
is TRUE
should the number of observations be added?
should the intercept(s) be included?
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()
should the model be kept as an attribute of the final result?
logical argument whether broom.helpers should not return
a message when requested output cannot be generated. Default is FALSE
logical argument whether broom.helpers should return an error
when requested output cannot be generated. Default is FALSE
other arguments passed to tidy_fun()
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()
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
}
}