Package 'broom.helpers'

Description

Remove backticks around variable names

Usage

.clean_backticks(x, variable_names = x)

Arguments

See Also

Other other_helpers: .escape_regex()

Description

This functions has been adapted from Hmisc::escapeRegex()

Usage

.escape_regex(string)

Arguments

See Also

Other other_helpers: .clean_backticks()

Description

The function .assert_package() checks whether a package is installed and returns an error or FALSE if not available. If a package search is provided, the function will check whether a minimum version of a package is required. The function .get_package_dependencies() returns a tibble with all dependencies of a specific package. Finally, .get_min_version_required() will return, if any, the minimum version of pkg required by pkg_search, NULL if no minimum version required.

Usage

.assert_package(pkg, fn = NULL, pkg_search = "broom.helpers", boolean = FALSE)

.get_package_dependencies(pkg_search = "broom.helpers")

.get_all_packages_dependencies(
  pkg_search = NULL,
  remove_duplicates = FALSE,
  lib.loc = NULL
)

.get_min_version_required(pkg, pkg_search = "broom.helpers")

Arguments

Details

get_all_packages_dependencies() could be used to get the list of dependencies of all installed packages.

Value

logical or error for .assert_package(), NULL or character with the minimum version required for .get_min_version_required(), a tibble for .get_package_dependencies().

Examples

.assert_package("broom", boolean = TRUE)
.get_package_dependencies()
.get_min_version_required("brms")

Description

Used for model_get_n(). For each row and term, equal 1 if this row should be taken into account in the estimate of the number of observations, 0 otherwise.

Usage

model_compute_terms_contributions(model)

## Default S3 method:
model_compute_terms_contributions(model)

Arguments

Details

This function does not cover lavaan models (NULL is returned).

See Also

Other model_helpers: model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

mod <- lm(Sepal.Length ~ Sepal.Width, iris)
mod |> model_compute_terms_contributions()

mod <- lm(hp ~ mpg + factor(cyl) + disp:hp, mtcars)
mod |> model_compute_terms_contributions()

mod <- glm(
  response ~ stage * grade + trt,
  gtsummary::trial,
  family = binomial,
  contrasts = list(
    stage = contr.sum,
    grade = contr.treatment(3, 2),
    trt = "contr.SAS"
  )
)
mod |> model_compute_terms_contributions()

mod <- glm(
  response ~ stage * trt,
  gtsummary::trial,
  family = binomial,
  contrasts = list(stage = contr.poly)
)
mod |> model_compute_terms_contributions()

mod <- glm(
  Survived ~ Class * Age + Sex,
  data = Titanic |> as.data.frame(),
  weights = Freq, family = binomial
)
mod |> model_compute_terms_contributions()

d <- dplyr::as_tibble(Titanic) |>
  dplyr::group_by(Class, Sex, Age) |>
  dplyr::summarise(
    n_survived = sum(n * (Survived == "Yes")),
    n_dead = sum(n * (Survived == "No"))
  )
mod <- glm(cbind(n_survived, n_dead) ~ Class * Age + Sex, data = d, family = binomial)
mod |> model_compute_terms_contributions()

Description

Return the assign attribute attached to the object returned by stats::model.matrix().

Usage

model_get_assign(model)

## Default S3 method:
model_get_assign(model)

## S3 method for class 'vglm'
model_get_assign(model)

## S3 method for class 'model_fit'
model_get_assign(model)

Arguments

See Also

stats::model.matrix()

Other model_helpers: model_compute_terms_contributions(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl), mtcars) |>
  model_get_assign()

Description

Indicate the type of coefficient among "generic", "logistic", "poisson", "relative_risk" or "prop_hazard".

Usage

model_get_coefficients_type(model)

## Default S3 method:
model_get_coefficients_type(model)

## S3 method for class 'glm'
model_get_coefficients_type(model)

## S3 method for class 'negbin'
model_get_coefficients_type(model)

## S3 method for class 'geeglm'
model_get_coefficients_type(model)

## S3 method for class 'fixest'
model_get_coefficients_type(model)

## S3 method for class 'biglm'
model_get_coefficients_type(model)

## S3 method for class 'glmerMod'
model_get_coefficients_type(model)

## S3 method for class 'clogit'
model_get_coefficients_type(model)

## S3 method for class 'polr'
model_get_coefficients_type(model)

## S3 method for class 'multinom'
model_get_coefficients_type(model)

## S3 method for class 'svyolr'
model_get_coefficients_type(model)

## S3 method for class 'clm'
model_get_coefficients_type(model)

## S3 method for class 'clmm'
model_get_coefficients_type(model)

## S3 method for class 'coxph'
model_get_coefficients_type(model)

## S3 method for class 'crr'
model_get_coefficients_type(model)

## S3 method for class 'tidycrr'
model_get_coefficients_type(model)

## S3 method for class 'cch'
model_get_coefficients_type(model)

## S3 method for class 'model_fit'
model_get_coefficients_type(model)

## S3 method for class 'LORgee'
model_get_coefficients_type(model)

Arguments

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl), mtcars) |>
  model_get_coefficients_type()

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
glm(Survived ~ Class + Age * Sex, data = df, weights = df$n, family = binomial) |>
  model_get_coefficients_type()

Description

Get contrasts used in the model

Usage

model_get_contrasts(model)

## S3 method for class 'model_fit'
model_get_contrasts(model)

## S3 method for class 'zeroinfl'
model_get_contrasts(model)

## S3 method for class 'hurdle'
model_get_contrasts(model)

## S3 method for class 'betareg'
model_get_contrasts(model)

Arguments

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

glm(
  am ~ mpg + factor(cyl),
  data = mtcars,
  family = binomial,
  contrasts = list(`factor(cyl)` = contr.sum)
) |>
  model_get_contrasts()

Description

Most model objects are proper R model objects. There are, however, some model objects that store the proper object internally (e.g. mice models). This function extracts that model object in those cases.

Usage

model_get_model(model)

## Default S3 method:
model_get_model(model)

## S3 method for class 'mira'
model_get_model(model)

Arguments

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl), mtcars) |>
  model_get_model()

Description

The structure of the object returned by stats::model.frame() could slightly differ for certain types of models. model_get_model_frame() will always return an object with the same data structure or NULL if it is not possible to compute model frame from model.

Usage

model_get_model_frame(model)

## Default S3 method:
model_get_model_frame(model)

## S3 method for class 'coxph'
model_get_model_frame(model)

## S3 method for class 'svycoxph'
model_get_model_frame(model)

## S3 method for class 'survreg'
model_get_model_frame(model)

## S3 method for class 'biglm'
model_get_model_frame(model)

## S3 method for class 'model_fit'
model_get_model_frame(model)

## S3 method for class 'fixest'
model_get_model_frame(model)

Arguments

See Also

stats::model.frame()

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl), mtcars) |>
  model_get_model_frame() |>
  head()

Description

The structure of the object returned by stats::model.matrix() could slightly differ for certain types of models. model_get_model_matrix() will always return an object with the same structure as stats::model.matrix.default().

Usage

model_get_model_matrix(model, ...)

## Default S3 method:
model_get_model_matrix(model, ...)

## S3 method for class 'multinom'
model_get_model_matrix(model, ...)

## S3 method for class 'clm'
model_get_model_matrix(model, ...)

## S3 method for class 'brmsfit'
model_get_model_matrix(model, ...)

## S3 method for class 'glmmTMB'
model_get_model_matrix(model, ...)

## S3 method for class 'plm'
model_get_model_matrix(model, ...)

## S3 method for class 'biglm'
model_get_model_matrix(model, ...)

## S3 method for class 'model_fit'
model_get_model_matrix(model, ...)

## S3 method for class 'fixest'
model_get_model_matrix(model, ...)

## S3 method for class 'LORgee'
model_get_model_matrix(model, ...)

## S3 method for class 'betareg'
model_get_model_matrix(model, ...)

## S3 method for class 'cch'
model_get_model_matrix(model, ...)

## S3 method for class 'cch'
model_get_terms(model, ...)

Arguments

Details

For models fitted with glmmTMB::glmmTMB(), it will return a model matrix taking into account all components ("cond", "zi" and "disp"). For a more restricted model matrix, please refer to glmmTMB::model.matrix.glmmTMB().

For plm::plm() models, constant columns are not removed.

See Also

stats::model.matrix()

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl), mtcars) |>
  model_get_model_matrix() |>
  head()

Description

For binomial and multinomial logistic models, will also return the number of events.

Usage

model_get_n(model)

## Default S3 method:
model_get_n(model)

## S3 method for class 'glm'
model_get_n(model)

## S3 method for class 'glmerMod'
model_get_n(model)

## S3 method for class 'multinom'
model_get_n(model)

## S3 method for class 'LORgee'
model_get_n(model)

## S3 method for class 'coxph'
model_get_n(model)

## S3 method for class 'survreg'
model_get_n(model)

## S3 method for class 'model_fit'
model_get_n(model)

## S3 method for class 'tidycrr'
model_get_n(model)

Arguments

Details

For Poisson models, will return the number of events and exposure time (defined with stats::offset()).

For Cox models (survival::coxph()), will return the number of events, exposure time and the number of individuals.

For competing risk regression models (tidycmprsk::crr()), n_event takes into account only the event of interest defined by failcode.

See tidy_add_n() for more details.

The total number of observations (N_obs), of individuals (N_ind), of events (N_event) and of exposure time (Exposure) are stored as attributes of the returned tibble.

This function does not cover lavaan models (NULL is returned).

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl) + disp:hp, mtcars) |>
  model_get_n()

mod <- glm(
  response ~ stage * grade + trt,
  gtsummary::trial,
  family = binomial,
  contrasts = list(stage = contr.sum, grade = contr.treatment(3, 2), trt = "contr.SAS")
)
mod |> model_get_n()

## Not run: 
mod <- glm(
  Survived ~ Class * Age + Sex,
  data = Titanic |> as.data.frame(),
  weights = Freq, family = binomial
)
mod |> model_get_n()

d <- dplyr::as_tibble(Titanic) |>
  dplyr::group_by(Class, Sex, Age) |>
  dplyr::summarise(
    n_survived = sum(n * (Survived == "Yes")),
    n_dead = sum(n * (Survived == "No"))
  )
mod <- glm(cbind(n_survived, n_dead) ~ Class * Age + Sex, data = d, family = binomial)
mod |> model_get_n()

mod <- glm(response ~ age + grade * trt, gtsummary::trial, family = poisson)
mod |> model_get_n()

mod <- glm(
  response ~ trt * grade + offset(ttdeath),
  gtsummary::trial,
  family = poisson
)
mod |> model_get_n()

dont
df <- survival::lung |> dplyr::mutate(sex = factor(sex))
mod <- survival::coxph(survival::Surv(time, status) ~ ph.ecog + age + sex, data = df)
mod |> model_get_n()

mod <- lme4::lmer(Reaction ~ Days + (Days | Subject), lme4::sleepstudy)
mod |> model_get_n()

mod <- lme4::glmer(response ~ trt * grade + (1 | stage),
  family = binomial, data = gtsummary::trial
)
mod |> model_get_n()

mod <- lme4::glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
  family = binomial, data = lme4::cbpp
)
mod |> model_get_n()

## End(Not run)

Description

Get the number of levels for each factor used in xlevels

Usage

model_get_nlevels(model)

## Default S3 method:
model_get_nlevels(model)

Arguments

Value

a tibble with two columns: "variable" and "var_nlevels"

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl), mtcars) |>
  model_get_nlevels()

Description

This function does not cover lavaan models (NULL is returned).

Usage

model_get_offset(model)

## Default S3 method:
model_get_offset(model)

Arguments

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

mod <- glm(
  response ~ trt + offset(log(ttdeath)),
  gtsummary::trial,
  family = poisson
)
mod |> model_get_offset()

Description

It is computed with emmeans::emmeans().

Usage

model_get_pairwise_contrasts(
  model,
  variables,
  pairwise_reverse = TRUE,
  contrasts_adjust = NULL,
  conf.level = 0.95,
  emmeans_args = list()
)

Arguments

Details

For pscl::zeroinfl() and pscl::hurdle() models, pairwise contrasts are computed separately for each component, using mode = "count" and mode = "zero" (see documentation of emmeans) and a component column is added to the results. This support is still experimental.

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

if (.assert_package("emmeans", boolean = TRUE)) {
  mod <- lm(Sepal.Length ~ Species, data = iris)
  mod |> model_get_pairwise_contrasts(variables = "Species")
  mod |>
    model_get_pairwise_contrasts(
      variables = "Species",
      contrasts_adjust = "none"
    )
}

Description

This function does not cover lavaan models (NULL is returned).

Usage

model_get_response(model)

## Default S3 method:
model_get_response(model)

## S3 method for class 'glm'
model_get_response(model)

## S3 method for class 'glmerMod'
model_get_response(model)

## S3 method for class 'model_fit'
model_get_response(model)

Arguments

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl) + disp:hp, mtcars) |>
  model_get_response()

mod <- glm(
  response ~ stage * grade + trt,
  gtsummary::trial,
  family = binomial,
  contrasts = list(stage = contr.sum, grade = contr.treatment(3, 2), trt = "contr.SAS")
)
mod |> model_get_response()

mod <- glm(
  Survived ~ Class * Age + Sex,
  data = Titanic |> as.data.frame(),
  weights = Freq,
  family = binomial
)
mod |> model_get_response()

d <- dplyr::as_tibble(Titanic) |>
  dplyr::group_by(Class, Sex, Age) |>
  dplyr::summarise(
    n_survived = sum(n * (Survived == "Yes")),
    n_dead = sum(n * (Survived == "No"))
  )
mod <- glm(cbind(n_survived, n_dead) ~ Class * Age + Sex, data = d, family = binomial, y = FALSE)
mod |> model_get_response()

Description

Get the name of the response variable

Usage

model_get_response_variable(model)

## Default S3 method:
model_get_response_variable(model)

Arguments

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl) + disp:hp, mtcars) |>
  model_get_response_variable()

mod <- glm(
  response ~ stage * grade + trt,
  gtsummary::trial,
  family = binomial
)
mod |> model_get_response_variable()

mod <- glm(
  Survived ~ Class * Age + Sex,
  data = Titanic |> as.data.frame(),
  weights = Freq,
  family = binomial
)
mod |> model_get_response_variable()

Description

Return the result of stats::terms() applied to the model or NULL if it is not possible to get terms from model.

Usage

model_get_terms(model)

## Default S3 method:
model_get_terms(model)

## S3 method for class 'brmsfit'
model_get_terms(model)

## S3 method for class 'glmmTMB'
model_get_terms(model)

## S3 method for class 'model_fit'
model_get_terms(model)

## S3 method for class 'betareg'
model_get_terms(model)

## S3 method for class 'betareg'
model_get_terms(model)

Arguments

Details

For models fitted with glmmTMB::glmmTMB(), it will return a terms object taking into account all components ("cond" and "zi"). For a more restricted terms object, please refer to glmmTMB::terms.glmmTMB().

See Also

stats::terms()

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl), mtcars) |>
  model_get_terms()

Description

This function does not cover lavaan models (NULL is returned).

Usage

model_get_weights(model)

## Default S3 method:
model_get_weights(model)

## S3 method for class 'svyglm'
model_get_weights(model)

## S3 method for class 'svrepglm'
model_get_weights(model)

## S3 method for class 'model_fit'
model_get_weights(model)

Arguments

Note

For class svrepglm objects (GLM on a survey object with replicate weights), it will return the original sampling weights of the data, not the replicate weights.

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

mod <- lm(Sepal.Length ~ Sepal.Width, iris)
mod |> model_get_weights()

mod <- lm(hp ~ mpg + factor(cyl) + disp:hp, mtcars, weights = mtcars$gear)
mod |> model_get_weights()

mod <- glm(
  response ~ stage * grade + trt,
  gtsummary::trial,
  family = binomial
)
mod |> model_get_weights()

mod <- glm(
  Survived ~ Class * Age + Sex,
  data = Titanic |> as.data.frame(),
  weights = Freq,
  family = binomial
)
mod |> model_get_weights()

d <- dplyr::as_tibble(Titanic) |>
  dplyr::group_by(Class, Sex, Age) |>
  dplyr::summarise(
    n_survived = sum(n * (Survived == "Yes")),
    n_dead = sum(n * (Survived == "No"))
  )
mod <- glm(cbind(n_survived, n_dead) ~ Class * Age + Sex, data = d, family = binomial)
mod |> model_get_weights()

Description

Get xlevels used in the model

Usage

model_get_xlevels(model)

## Default S3 method:
model_get_xlevels(model)

## S3 method for class 'lmerMod'
model_get_xlevels(model)

## S3 method for class 'glmerMod'
model_get_xlevels(model)

## S3 method for class 'felm'
model_get_xlevels(model)

## S3 method for class 'brmsfit'
model_get_xlevels(model)

## S3 method for class 'glmmTMB'
model_get_xlevels(model)

## S3 method for class 'plm'
model_get_xlevels(model)

## S3 method for class 'model_fit'
model_get_xlevels(model)

Arguments

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl), mtcars) |>
  model_get_xlevels()

Description

It will also identify interaction terms and intercept(s).

Usage

model_identify_variables(model)

## Default S3 method:
model_identify_variables(model)

## S3 method for class 'lavaan'
model_identify_variables(model)

## S3 method for class 'aov'
model_identify_variables(model)

## S3 method for class 'clm'
model_identify_variables(model)

## S3 method for class 'clmm'
model_identify_variables(model)

## S3 method for class 'gam'
model_identify_variables(model)

## S3 method for class 'model_fit'
model_identify_variables(model)

## S3 method for class 'logitr'
model_identify_variables(model)

Arguments

Value

A tibble with four columns:

• term: coefficients of the model

• variable: the corresponding variable

• var_class: class of the variable (cf. stats::.MFclass())

• var_type: "continuous", "dichotomous" (categorical variable with 2 levels), "categorical" (categorical variable with 3 or more levels), "intercept" or "interaction"

• var_nlevels: number of original levels for categorical variables

See Also

tidy_identify_variables()

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
glm(
  Survived ~ Class + Age * Sex,
  data = df, weights = df$n,
  family = binomial
) |>
  model_identify_variables()

iris |>
  lm(
    Sepal.Length ~ poly(Sepal.Width, 2) + Species,
    data = _,
    contrasts = list(Species = contr.sum)
  ) |>
  model_identify_variables()

Description

List contrasts used by a model

Usage

model_list_contrasts(model)

## Default S3 method:
model_list_contrasts(model)

Arguments

Details

For models with no intercept, no contrasts will be applied to one of the categorical variable. In such case, one dummy term will be returned for each level of the categorical variable.

Value

A tibble with three columns:

• variable: variable name

• contrasts: contrasts used

• contrasts_type: type of contrasts ("treatment", "sum", "poly", "helmert", "sdiff, "other" or "no.contrast")

• reference: for variables with treatment, SAS or sum contrasts, position of the reference level

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_higher_order_variables(), model_list_terms_levels(), model_list_variables()

Examples

glm(
  am ~ mpg + factor(cyl),
  data = mtcars,
  family = binomial,
  contrasts = list(`factor(cyl)` = contr.sum)
) |>
  model_list_contrasts()

Description

List higher order variables of a model

Usage

model_list_higher_order_variables(model)

## Default S3 method:
model_list_higher_order_variables(model)

Arguments

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_terms_levels(), model_list_variables()

Examples

lm(hp ~ mpg + factor(cyl) + disp:hp, mtcars) |>
  model_list_higher_order_variables()

mod <- glm(
  response ~ stage * grade + trt:stage,
  gtsummary::trial,
  family = binomial
)
mod |> model_list_higher_order_variables()

mod <- glm(
  Survived ~ Class * Age + Sex,
  data = Titanic |> as.data.frame(),
  weights = Freq,
  family = binomial
)
mod |> model_list_higher_order_variables()

Description

Only for categorical variables with treatment, SAS, sum or successive differences contrasts (cf. MASS::contr.sdif()), and categorical variables with no contrast.

Usage

model_list_terms_levels(
  model,
  label_pattern = "{level}",
  variable_labels = NULL,
  sdif_term_level = c("diff", "ratio")
)

## Default S3 method:
model_list_terms_levels(
  model,
  label_pattern = "{level}",
  variable_labels = NULL,
  sdif_term_level = c("diff", "ratio")
)

Arguments

Value

A tibble with ten columns:

• variable: variable

• contrasts_type: type of contrasts ("sum" or "treatment")

• term: term name

• level: term level

• level_rank: rank of the level

• reference: logical indicating which term is the reference level

• reference_level: level of the reference term

• var_label: variable label obtained with model_list_variables()

• var_nlevels: number of levels in this variable

• dichotomous: logical indicating if the variable is dichotomous

• label: term label (by default equal to term level) The first nine columns can be used in label_pattern.

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_variables()

Examples

glm(
  am ~ mpg + factor(cyl),
  data = mtcars,
  family = binomial,
  contrasts = list(`factor(cyl)` = contr.sum)
) |>
  model_list_terms_levels()

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))

mod <- glm(
  Survived ~ Class + Age + Sex,
  data = df, weights = df$n, family = binomial,
  contrasts = list(Age = contr.sum, Class = "contr.helmert")
)
mod |> model_list_terms_levels()
mod |> model_list_terms_levels("{level} vs {reference_level}")
mod |> model_list_terms_levels("{variable} [{level} - {reference_level}]")
mod |> model_list_terms_levels(
  "{ifelse(reference, level, paste(level, '-', reference_level))}"
)

Description

Including variables used only in an interaction.

Usage

model_list_variables(
  model,
  labels = NULL,
  only_variable = FALSE,
  add_var_type = FALSE
)

## Default S3 method:
model_list_variables(
  model,
  labels = NULL,
  only_variable = FALSE,
  add_var_type = FALSE
)

## S3 method for class 'lavaan'
model_list_variables(
  model,
  labels = NULL,
  only_variable = FALSE,
  add_var_type = FALSE
)

## S3 method for class 'logitr'
model_list_variables(
  model,
  labels = NULL,
  only_variable = FALSE,
  add_var_type = FALSE
)

Arguments

Value

A tibble with three columns:

• variable: the corresponding variable

• var_class: class of the variable (cf. stats::.MFclass())

• label_attr: variable label defined in the original data frame with the label attribute (cf. labelled::var_label())

• var_label: a variable label (by priority, labels if defined, label_attr if available, otherwise variable)

If add_var_type = TRUE:

• var_type: "continuous", "dichotomous" (categorical variable with 2 levels), "categorical" (categorical variable with 3 or more levels), "intercept" or "interaction"

• var_nlevels: number of original levels for categorical variables

See Also

Other model_helpers: model_compute_terms_contributions(), model_get_assign(), model_get_coefficients_type(), model_get_contrasts(), model_get_model(), model_get_model_frame(), model_get_model_matrix(), model_get_n(), model_get_nlevels(), model_get_offset(), model_get_pairwise_contrasts(), model_get_response(), model_get_response_variable(), model_get_terms(), model_get_weights(), model_get_xlevels(), model_identify_variables(), model_list_contrasts(), model_list_higher_order_variables(), model_list_terms_levels()

Examples

if (.assert_package("gtsummary", boolean = TRUE)) {
  df <- Titanic |>
    dplyr::as_tibble() |>
    dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
  glm(
    Survived ~ Class + Age:Sex,
    data = df, weights = df$n,
    family = binomial
  ) |>
  model_list_variables()

  iris |>
    lm(
      Sepal.Length ~ poly(Sepal.Width, 2) + Species,
      data = _,
      contrasts = list(Species = contr.sum)
    ) |>
    model_list_variables()

  glm(
    response ~ poly(age, 3) + stage + grade * trt,
    na.omit(gtsummary::trial),
    family = binomial,
  ) |>
    model_list_variables()
}

Description

This function uses the information from a model tidy tibble to generate a data frame exposing the different variables of the model, data frame that could be used for tidy selection. In addition, columns "var_type", "var_class" and "contrasts_type" are scoped and their values are added as attributes to the data frame. For example, if var_type='continuous' for variable "age", then the attribute attr(.$age, 'gtsummary.var_type') <- 'continuous' is set. That attribute is then used in a selector like all_continuous(). Note: attributes are prefixed with "gtsummary." to be compatible with selectors provided by {gtsummary}.

Usage

scope_tidy(x, data = NULL)

Arguments

Value

A data frame.

Examples

mod <- lm(Sepal.Length ~ Sepal.Width * Species, data = iris)
tt <- mod |> tidy_and_attach() |> tidy_add_contrasts()

scope_tidy(tt) |> str()
scope_tidy(tt, data = model_get_model_frame(mod)) |> str()

scope_tidy(tt) |> dplyr::select(dplyr::starts_with("Se")) |> names()
scope_tidy(tt) |> dplyr::select(where(is.factor)) |> names()
scope_tidy(tt) |> dplyr::select(all_continuous()) |> names()
scope_tidy(tt) |> dplyr::select(all_contrasts()) |> names()
scope_tidy(tt) |> dplyr::select(all_interaction()) |> names()
scope_tidy(tt) |> dplyr::select(all_intercepts()) |> names()

Description

Set of functions to supplement the tidyselect set of functions for selecting columns of data frames (and other items as well).

• all_continuous() selects continuous variables

• all_categorical() selects categorical (including "dichotomous") variables

• all_dichotomous() selects only type "dichotomous"

• all_interaction() selects interaction terms from a regression model

• all_intercepts() selects intercept terms from a regression model

• all_contrasts() selects variables in regression model based on their type of contrast

• all_ran_pars() and all_ran_vals() for random-effect parameters and values from a mixed model (see vignette("broom_mixed_intro", package = "broom.mixed"))

Usage

all_continuous(continuous2 = TRUE)

all_categorical(dichotomous = TRUE)

all_dichotomous()

all_interaction()

all_ran_pars()

all_ran_vals()

all_intercepts()

all_contrasts(
  contrasts_type = c("treatment", "sum", "poly", "helmert", "sdif", "other")
)

Arguments

Value

A character vector of column names selected.

See Also

scope_tidy()

Examples

glm(response ~ age * trt + grade, gtsummary::trial, family = binomial) |>
  tidy_plus_plus(exponentiate = TRUE, include = all_categorical())

glm(response ~ age + trt + grade + stage,
  gtsummary::trial,
  family = binomial,
  contrasts = list(trt = contr.SAS, grade = contr.sum, stage = contr.poly)
) |>
  tidy_plus_plus(
    exponentiate = TRUE,
    include = all_contrasts(c("treatment", "sum"))
  )

Description

Sequence generation between min and max

Usage

seq_range(x, length.out = 25)

Arguments

Details

seq_range(x, length.out) is a shortcut for seq(min(x, na.rm = TRUE), max(x, na.rm = TRUE), length.out = length.out)

Value

a numeric vector

Examples

seq_range(iris$Petal.Length)

Description

Listing of Supported Models

Usage

supported_models

Format

A data frame with one row per supported model

model

Model

notes

Notes

Supported models

Description

Add the type of coefficients ("generic", "logistic", "poisson", "relative_risk" or "prop_hazard") and the corresponding coefficient labels, as attributes to x (respectively named coefficients_type and coefficients_label).

Usage

tidy_add_coefficients_type(
  x,
  exponentiate = attr(x, "exponentiate"),
  model = tidy_get_model(x)
)

Arguments

See Also

Other tidy_helpers: 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_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

ex1 <- lm(hp ~ mpg + factor(cyl), mtcars) |>
  tidy_and_attach() |>
  tidy_add_coefficients_type()
attr(ex1, "coefficients_type")
attr(ex1, "coefficients_label")

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
ex2 <- glm(
  Survived ~ Class + Age * Sex,
  data = df,
  weights = df$n,
  family = binomial
) |>
  tidy_and_attach(exponentiate = TRUE) |>
  tidy_add_coefficients_type()
attr(ex2, "coefficients_type")
attr(ex2, "coefficients_label")

Description

Add a contrasts column corresponding to contrasts used for a categorical variable and a contrasts_type column equal to "treatment", "sum", "poly", "helmert", "other" or "no.contrast".

Usage

tidy_add_contrasts(x, model = tidy_get_model(x), quiet = FALSE)

Arguments

Details

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

See Also

Other tidy_helpers: tidy_add_coefficients_type(), 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_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))

glm(
  Survived ~ Class + Age + Sex,
  data = df, weights = df$n, family = binomial,
  contrasts = list(Age = contr.sum, Class = "contr.helmert")
) |>
  tidy_and_attach() |>
  tidy_add_contrasts()

Description

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

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 (.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()
}

Description

For variables with several terms (usually categorical variables but could also be the case of continuous variables with polynomial terms or splines), tidy_add_header_rows() will add an additional row per variable, where label will be equal to var_label. These additional rows could be identified with header_row column.

Usage

tidy_add_header_rows(
  x,
  show_single_row = NULL,
  model = tidy_get_model(x),
  quiet = FALSE,
  strict = FALSE
)

Arguments

Details

The show_single_row argument allows to specify a list of dichotomous variables that should be displayed on a single row instead of two rows.

The added header_row column will be equal to:

• TRUE for an header row;

• FALSE for a normal row of a variable with an header row;

• NA for variables without an header row.

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

See Also

Other tidy_helpers: tidy_add_coefficients_type(), tidy_add_contrasts(), tidy_add_estimate_to_reference_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 (.assert_package("gtsummary", boolean = TRUE)) {
  df <- Titanic |>
    dplyr::as_tibble() |>
    dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))

  res <-
    glm(
      Survived ~ Class + Age + Sex,
      data = df, weights = df$n, family = binomial,
      contrasts = list(Age = contr.sum, Class = "contr.SAS")
    ) |>
    tidy_and_attach() |>
    tidy_add_variable_labels(labels = list(Class = "Custom label for Class")) |>
    tidy_add_reference_rows()
  res |> tidy_add_header_rows()
  res |> tidy_add_header_rows(show_single_row = all_dichotomous())

  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_header_rows()
}

Description

Add the number of observations in a new column n_obs, taking into account any weights if they have been defined.

Usage

tidy_add_n(x, model = tidy_get_model(x))

Arguments

Details

For continuous variables, it corresponds to all valid observations contributing to the model.

For categorical variables coded with treatment or sum contrasts, each model term could be associated to only one level of the original categorical variable. Therefore, n_obs will correspond to the number of observations associated with that level. n_obs will also be computed for reference rows. For polynomial contrasts (defined with stats::contr.poly()), all levels will contribute to the computation of each model term. Therefore, n_obs will be equal to the total number of observations. For Helmert and custom contrasts, only rows contributing positively (i.e. with a positive contrast) to the computation of a term will be considered for estimating n_obs. The result could therefore be difficult to interpret. For a better understanding of which observations are taken into account to compute n_obs values, you could look at model_compute_terms_contributions().

For interaction terms, only rows contributing to all the terms of the interaction will be considered to compute n_obs.

For binomial logistic models, tidy_add_n() will also return the corresponding number of events (n_event) for each term, taking into account any defined weights. Observed proportions could be obtained as n_obs / n_event.

Similarly, a number of events will be computed for multinomial logistic models (nnet::multinom()) for each level of the outcome (y.level), corresponding to the number of observations equal to that outcome level.

For Poisson models, n_event will be equal to the number of counts per term. In addition, a third column exposure will be computed. If no offset is defined, exposure is assumed to be equal to 1 (eventually multiplied by weights) per observation. If an offset is defined, exposure will be equal to the (weighted) sum of the exponential of the offset (as a reminder, to model the effect of x on the ratio y / z, a Poisson model will be defined as glm(y ~ x + offset(log(z)), family = poisson)). Observed rates could be obtained with n_event / exposure.

For Cox models (survival::coxph()), an individual could be coded with several observations (several rows). n_obs will correspond to the weighted number of observations which could be different from the number of individuals n_ind. tidy_add_n() will also compute a (weighted) number of events (n_event) according to the definition of the survival::Surv() object. Exposure time is also returned in exposure column. It is equal to the (weighted) sum of the time variable if only one variable time is passed to survival::Surv(), and to the (weighted) sum of time2 - time if two time variables are defined in survival::Surv().

For competing risk regression models (tidycmprsk::crr()), n_event takes into account only the event of interest defined by failcode.

The (weighted) total number of observations (N_obs), of individuals (N_ind), of events (N_event) and of exposure time (Exposure) are stored as attributes of the returned tibble.

See Also

Other tidy_helpers: tidy_add_coefficients_type(), tidy_add_contrasts(), tidy_add_estimate_to_reference_rows(), tidy_add_header_rows(), 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

lm(Petal.Length ~ ., data = iris) |>
  tidy_and_attach() |>
  tidy_add_n()

lm(Petal.Length ~ ., data = iris, contrasts = list(Species = contr.sum)) |>
  tidy_and_attach() |>
  tidy_add_n()

lm(Petal.Length ~ ., data = iris, contrasts = list(Species = contr.poly)) |>
  tidy_and_attach() |>
  tidy_add_n()

lm(Petal.Length ~ poly(Sepal.Length, 2), data = iris) |>
  tidy_and_attach() |>
  tidy_add_n()

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))

glm(
  Survived ~ Class + Age + Sex,
  data = df, weights = df$n, family = binomial,
  contrasts = list(Age = contr.sum, Class = "contr.helmert")
) |>
  tidy_and_attach() |>
  tidy_add_n()

glm(
  Survived ~ Class * (Age:Sex),
  data = df, weights = df$n, family = binomial,
  contrasts = list(Age = contr.sum, Class = "contr.helmert")
) |>
  tidy_and_attach() |>
  tidy_add_n()

glm(response ~ age + grade * trt, gtsummary::trial, family = poisson) |>
  tidy_and_attach() |>
  tidy_add_n()

glm(
  response ~ trt * grade + offset(log(ttdeath)),
  gtsummary::trial,
  family = poisson
) |>
  tidy_and_attach() |>
  tidy_add_n()

Description

Computes pairwise contrasts with emmeans::emmeans() and add them to the results tibble. Works only with models supported by emmeans, see vignette("models", package = "emmeans").

Usage

tidy_add_pairwise_contrasts(
  x,
  variables = all_categorical(),
  keep_model_terms = FALSE,
  pairwise_reverse = TRUE,
  contrasts_adjust = NULL,
  conf.level = attr(x, "conf.level"),
  emmeans_args = list(),
  model = tidy_get_model(x),
  quiet = FALSE
)

Arguments

Note

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

For multi-components models, such as zero-inflated Poisson or beta regression, support of pairwise contrasts is still experimental.

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_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 (.assert_package("emmeans", boolean = TRUE)) {
  mod1 <- lm(Sepal.Length ~ Species, data = iris)
  mod1 |>
    tidy_and_attach() |>
    tidy_add_pairwise_contrasts()

  mod1 |>
    tidy_and_attach() |>
    tidy_add_pairwise_contrasts(pairwise_reverse = FALSE)

  mod1 |>
    tidy_and_attach() |>
    tidy_add_pairwise_contrasts(keep_model_terms = TRUE)

  mod1 |>
    tidy_and_attach() |>
    tidy_add_pairwise_contrasts(contrasts_adjust = "none")

  if (.assert_package("gtsummary", boolean = TRUE)) {
    mod2 <- glm(
      response ~ age + trt + grade,
      data = gtsummary::trial,
      family = binomial
    )
    mod2 |>
      tidy_and_attach(exponentiate = TRUE) |>
      tidy_add_pairwise_contrasts()
  }
}

Description

For categorical variables with a treatment contrast (stats::contr.treatment()), a SAS contrast (stats::contr.SAS()) a sum contrast (stats::contr.sum()), or successive differences contrast (MASS::contr.sdif()) add a reference row.

Usage

tidy_add_reference_rows(
  x,
  no_reference_row = NULL,
  model = tidy_get_model(x),
  quiet = FALSE
)

Arguments

Details

The added reference_row column will be equal to:

• TRUE for a reference row;

• FALSE for a normal row of a variable with a reference row;

• NA for variables without a reference row.

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

tidy_add_reference_rows() will not populate the label of the reference term. It is therefore better to apply tidy_add_term_labels() after tidy_add_reference_rows() rather than before. Similarly, it is better to apply tidy_add_reference_rows() before tidy_add_n().

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_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 (.assert_package("gtsummary", boolean = TRUE)) {
  df <- Titanic |>
    dplyr::as_tibble() |>
    dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))

  res <-
    glm(
      Survived ~ Class + Age + Sex,
      data = df, weights = df$n, family = binomial,
      contrasts = list(Age = contr.sum, Class = "contr.SAS")
    ) |>
    tidy_and_attach()
  res |> tidy_add_reference_rows()
  res |> tidy_add_reference_rows(no_reference_row = all_dichotomous())
  res |> tidy_add_reference_rows(no_reference_row = "Class")

  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()
}

Description

Will add term labels in a label column, based on:

1. labels provided in labels argument if provided;

2. factor levels for categorical variables coded with treatment, SAS or sum contrasts (the label could be customized with categorical_terms_pattern argument);

3. variable labels when there is only one term per variable;

4. term name otherwise.

Usage

tidy_add_term_labels(
  x,
  labels = NULL,
  interaction_sep = " * ",
  categorical_terms_pattern = "{level}",
  model = tidy_get_model(x),
  quiet = FALSE,
  strict = FALSE
)

Arguments

Details

If the variable_label column is not yet available in x, tidy_add_variable_labels() will be automatically applied. If the contrasts column is not yet available in x, tidy_add_contrasts() will be automatically applied.

It is possible to pass a custom label for any term in labels, including interaction terms.

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_variable_labels(), tidy_attach_model(), tidy_disambiguate_terms(), tidy_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes"))) |>
  labelled::set_variable_labels(
    Class = "Passenger's class",
    Sex = "Sex"
  )

mod <-
  glm(Survived ~ Class * Age * Sex, data = df, weights = df$n, family = binomial)
mod |>
  tidy_and_attach() |>
  tidy_add_term_labels()
mod |>
  tidy_and_attach() |>
  tidy_add_term_labels(
    interaction_sep = " x ",
    categorical_terms_pattern = "{level} / {reference_level}"
  )

Description

Will add variable labels in a var_label column, based on:

1. labels provided in labels argument if provided;

2. variable labels defined in the original data frame with the label attribute (cf. labelled::var_label());

3. variable name otherwise.

Usage

tidy_add_variable_labels(
  x,
  labels = NULL,
  interaction_sep = " * ",
  model = tidy_get_model(x)
)

Arguments

Details

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

It is possible to pass a custom label for an interaction term in labels (see examples).

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_attach_model(), tidy_disambiguate_terms(), tidy_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes"))) |>
  labelled::set_variable_labels(
    Class = "Passenger's class",
    Sex = "Sex"
  )

glm(Survived ~ Class * Age * Sex, data = df, weights = df$n, family = binomial) |>
  tidy_and_attach() |>
  tidy_add_variable_labels(
    labels = list(
      "(Intercept)" ~ "Custom intercept",
      Sex ~ "Gender",
      "Class:Age" ~ "Custom label"
    )
  )

Description

Use effects::allEffects() to estimate marginal predictions and return a tibble tidied in a way that it could be used by broom.helpers functions. See vignette("functions-supported-by-effects", package = "effects") for a list of supported models.

Usage

tidy_all_effects(x, conf.int = TRUE, conf.level = 0.95, ...)

Arguments

Details

By default, effects::allEffects() estimate marginal predictions at the mean at the observed means for continuous variables and weighting modalities of categorical variables according to their observed distribution in the original dataset. Marginal predictions are therefore computed at a sort of averaged situation / typical values for the other variables fixed in the model.

For more information, see vignette("marginal_tidiers", "broom.helpers").

Note

If the model contains interactions, effects::allEffects() will return marginal predictions for the different levels of the interactions.

See Also

effects::allEffects()

Other marginal_tieders: tidy_avg_comparisons(), tidy_avg_slopes(), tidy_ggpredict(), tidy_marginal_contrasts(), tidy_marginal_means(), tidy_marginal_predictions(), tidy_margins()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  tidyr::uncount(n) |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
mod <- glm(
  Survived ~ Class + Age + Sex,
  data = df, family = binomial
)
tidy_all_effects(mod)
tidy_plus_plus(mod, tidy_fun = tidy_all_effects)

Description

To facilitate the use of broom helpers with pipe, it is recommended to attach the original model as an attribute to the tibble of model terms generated by broom::tidy().

Usage

tidy_attach_model(x, model, .attributes = NULL)

tidy_and_attach(
  model,
  tidy_fun = tidy_with_broom_or_parameters,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  model_matrix_attr = TRUE,
  ...
)

tidy_get_model(x)

tidy_detach_model(x)

Arguments

Details

tidy_attach_model() attach the model to a tibble already generated while tidy_and_attach() will apply broom::tidy() and attach the model.

Use tidy_get_model() to get the model attached to the tibble and tidy_detach_model() to remove the attribute containing the 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_disambiguate_terms(), tidy_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

mod <- lm(Sepal.Length ~ Sepal.Width + Species, data = iris)
tt <- mod |>
  tidy_and_attach(conf.int = TRUE)
tt
tidy_get_model(tt)

Description

Use marginaleffects::avg_comparisons() to estimate marginal contrasts and return a tibble tidied in a way that it could be used by broom.helpers functions. See marginaleffects::avg_comparisons() for a list of supported models.

Usage

tidy_avg_comparisons(x, conf.int = TRUE, conf.level = 0.95, ...)

Arguments

Details

By default, marginaleffects::avg_comparisons() estimate average marginal contrasts: a contrast is computed for each observed value in the original dataset (counterfactual approach) before being averaged. Marginal Contrasts at the Mean could be computed by specifying newdata = "mean". The variables argument can be used to select the contrasts to be computed. Please refer to the documentation page of marginaleffects::avg_comparisons().

See also tidy_marginal_contrasts() for taking into account interactions. For more information, see vignette("marginal_tidiers", "broom.helpers").

See Also

marginaleffects::avg_comparisons()

Other marginal_tieders: tidy_all_effects(), tidy_avg_slopes(), tidy_ggpredict(), tidy_marginal_contrasts(), tidy_marginal_means(), tidy_marginal_predictions(), tidy_margins()

Examples

# Average Marginal Contrasts

df <- Titanic |>
  dplyr::as_tibble() |>
  tidyr::uncount(n) |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
mod <- glm(
  Survived ~ Class + Age + Sex,
  data = df, family = binomial
)
tidy_avg_comparisons(mod)
tidy_plus_plus(mod, tidy_fun = tidy_avg_comparisons)

mod2 <- lm(Petal.Length ~ poly(Petal.Width, 2) + Species, data = iris)
tidy_avg_comparisons(mod2)

# Custumizing the type of contrasts
tidy_avg_comparisons(
  mod2,
  variables = list(Petal.Width = 2, Species = "pairwise")
)

# Marginal Contrasts at the Mean
tidy_avg_comparisons(mod, newdata = "mean")
tidy_plus_plus(mod, tidy_fun = tidy_avg_comparisons, newdata = "mean")

Description

Use marginaleffects::avg_slopes() to estimate marginal slopes / effects and return a tibble tidied in a way that it could be used by broom.helpers functions. See marginaleffects::avg_slopes() for a list of supported models.

Usage

tidy_avg_slopes(x, conf.int = TRUE, conf.level = 0.95, ...)

Arguments

Details

By default, marginaleffects::avg_slopes() estimate average marginal effects (AME): an effect is computed for each observed value in the original dataset before being averaged. Marginal Effects at the Mean (MEM) could be computed by specifying newdata = "mean". Other types of marginal effects could be computed. Please refer to the documentation page of marginaleffects::avg_slopes().

For more information, see vignette("marginal_tidiers", "broom.helpers").

See Also

marginaleffects::avg_slopes()

Other marginal_tieders: tidy_all_effects(), tidy_avg_comparisons(), tidy_ggpredict(), tidy_marginal_contrasts(), tidy_marginal_means(), tidy_marginal_predictions(), tidy_margins()

Examples

# Average Marginal Effects (AME)

df <- Titanic |>
  dplyr::as_tibble() |>
  tidyr::uncount(n) |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
mod <- glm(
  Survived ~ Class + Age + Sex,
  data = df, family = binomial
)
tidy_avg_slopes(mod)
tidy_plus_plus(mod, tidy_fun = tidy_avg_slopes)

mod2 <- lm(Petal.Length ~ poly(Petal.Width, 2) + Species, data = iris)
tidy_avg_slopes(mod2)

# Marginal Effects at the Mean (MEM)
tidy_avg_slopes(mod, newdata = "mean")
tidy_plus_plus(mod, tidy_fun = tidy_avg_slopes, newdata = "mean")

Description

Tidy with broom::tidy() and checks that all arguments are used

Usage

tidy_broom(x, ...)

Arguments

See Also

Other custom_tieders: tidy_multgee(), tidy_parameters(), tidy_with_broom_or_parameters(), tidy_zeroinfl()

Description

For mixed models, the term column returned by broom.mixed may have duplicated values for random-effect parameters and random-effect values. In such case, the terms could be disambiguated be prefixing them with the value of the group column. tidy_disambiguate_terms() will not change any term if there is no group column in x. The original term value is kept in a new column original_term.

Usage

tidy_disambiguate_terms(x, sep = ".", model = tidy_get_model(x), quiet = FALSE)

Arguments

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_identify_variables(), tidy_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

if (
  .assert_package("lme4", boolean = TRUE) &&
    .assert_package("broom.mixed", boolean = TRUE) &&
    .assert_package("gtsummary", boolean = TRUE)
) {
  mod <- lme4::lmer(marker ~ stage + (1 | grade) + (death | response), gtsummary::trial)
  mod |>
    tidy_and_attach() |>
    tidy_disambiguate_terms()
}

Description

Use ggeffects::ggpredict() to estimate marginal predictions and return a tibble tidied in a way that it could be used by broom.helpers functions. See https://strengejacke.github.io/ggeffects/ for a list of supported models.

Usage

tidy_ggpredict(x, conf.int = TRUE, conf.level = 0.95, ...)

Arguments

Details

By default, ggeffects::ggpredict() estimate marginal predictions at the observed mean of continuous variables and at the first modality of categorical variables (regardless of the type of contrasts used in the model).

For more information, see vignette("marginal_tidiers", "broom.helpers").

Note

By default, ggeffects::ggpredict() estimates marginal predictions for each individual variable, regardless of eventual interactions.

See Also

ggeffects::ggpredict()

Other marginal_tieders: tidy_all_effects(), tidy_avg_comparisons(), tidy_avg_slopes(), tidy_marginal_contrasts(), tidy_marginal_means(), tidy_marginal_predictions(), tidy_margins()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  tidyr::uncount(n) |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
mod <- glm(
  Survived ~ Class + Age + Sex,
  data = df, family = binomial
)
tidy_ggpredict(mod)
tidy_plus_plus(mod, tidy_fun = tidy_ggpredict)

Description

tidy_identify_variables() will add to the tidy tibble three additional columns: variable, var_class, var_type and var_nlevels.

Usage

tidy_identify_variables(x, model = tidy_get_model(x), quiet = FALSE)

Arguments

Details

It will also identify interaction terms and intercept(s).

var_type could be:

• "continuous",

• "dichotomous" (categorical variable with 2 levels),

• "categorical" (categorical variable with 3 levels or more),

• "intercept"

• "interaction"

• ⁠"ran_pars⁠ (random-effect parameters for mixed models)

• "ran_vals" (random-effect values for mixed models)

• "unknown" in the rare cases where tidy_identify_variables() will fail to identify the list of variables

For dichotomous and categorical variables, var_nlevels corresponds to the number of original levels in the corresponding variables.

See Also

model_identify_variables()

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_plus_plus(), tidy_remove_intercept(), tidy_select_variables()

Examples

df <- Titanic |>
  dplyr::as_tibble() |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
glm(
  Survived ~ Class + Age * Sex,
  data = df,
  weights = df$n,
  family = binomial
) |>
  tidy_and_attach() |>
  tidy_identify_variables()

lm(
  Sepal.Length ~ poly(Sepal.Width, 2) + Species,
  data = iris,
  contrasts = list(Species = contr.sum)
) |>
  tidy_and_attach(conf.int = TRUE) |>
  tidy_identify_variables()

Description

Use marginaleffects::avg_comparisons() to estimate marginal contrasts for each variable of a model and return a tibble tidied in a way that it could be used by broom.helpers functions. See marginaleffects::avg_comparisons() for a list of supported models.

Usage

tidy_marginal_contrasts(
  x,
  variables_list = "auto",
  conf.int = TRUE,
  conf.level = 0.95,
  ...
)

variables_to_contrast(
  model,
  interactions = TRUE,
  cross = FALSE,
  var_categorical = "reference",
  var_continuous = 1,
  by_categorical = unique,
  by_continuous = stats::fivenum
)

Arguments

Details

Marginal contrasts are obtained by calling, for each variable or combination of variables, marginaleffects::avg_comparisons().

tidy_marginal_contrasts() will compute marginal contrasts for each variable or combination of variables, before stacking the results in a unique tibble. This is why tidy_marginal_contrasts() has a variables_list argument consisting of a list of specifications that will be passed sequentially to the variables and the by argument of marginaleffects::avg_comparisons().

Considering a single categorical variable named cat, tidy_marginal_contrasts() will call avg_comparisons(model, variables = list(cat = "reference")) to obtain average marginal contrasts for this variable.

Considering a single continuous variable named cont, tidy_marginalcontrasts() will call avg_comparisons(model, variables = list(cont = 1)) to obtain average marginal contrasts for an increase of one unit.

For a combination of variables, there are several possibilities. You could compute "cross-contrasts" by providing simultaneously several variables to variables and specifying cross = TRUE to marginaleffects::avg_comparisons(). Alternatively, you could compute the contrasts of a first variable specified to variables for the different values of a second variable specified to by.

The helper function variables_to_contrast() could be used to automatically generate a suitable list to be used with variables_list. Each combination of variables should be a list with two named elements: "variables" a list of named elements passed to variables and "by" a list of named elements used for creating a relevant datagrid and whose names are passed to by.

variables_list's default value, "auto", calls variables_to_contrast(interactions = TRUE, cross = FALSE) while "no_interaction" is a shortcut for variables_to_contrast(interactions = FALSE). "cross" calls variables_to_contrast(interactions = TRUE, cross = TRUE)

You can also provide custom specifications (see examples).

By default, average marginal contrasts are computed: contrasts are computed using a counterfactual grid for each value of the variable of interest, before averaging the results. Marginal contrasts at the mean could be obtained by indicating newdata = "mean". Other assumptions are possible, see the help file of marginaleffects::avg_comparisons().

For more information, see vignette("marginal_tidiers", "broom.helpers").

See Also

marginaleffects::avg_comparisons(), tidy_avg_comparisons()

Other marginal_tieders: tidy_all_effects(), tidy_avg_comparisons(), tidy_avg_slopes(), tidy_ggpredict(), tidy_marginal_means(), tidy_marginal_predictions(), tidy_margins()

Examples

# Average Marginal Contrasts
df <- Titanic |>
  dplyr::as_tibble() |>
  tidyr::uncount(n) |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
mod <- glm(
  Survived ~ Class + Age + Sex,
  data = df, family = binomial
)
tidy_marginal_contrasts(mod)
tidy_plus_plus(mod, tidy_fun = tidy_marginal_contrasts)

mod2 <- lm(Petal.Length ~ poly(Petal.Width, 2) + Species, data = iris)
tidy_marginal_contrasts(mod2)
tidy_marginal_contrasts(
  mod2,
  variables_list = variables_to_predict(
    mod2,
    continuous = 3,
    categorical = "pairwise"
  )
)

# Model with interactions
mod3 <- glm(
  Survived ~ Sex * Age + Class,
  data = df, family = binomial
)
tidy_marginal_contrasts(mod3)
tidy_marginal_contrasts(mod3, "no_interaction")
tidy_marginal_contrasts(mod3, "cross")
tidy_marginal_contrasts(
  mod3,
  variables_list = list(
    list(variables = list(Class = "pairwise"), by = list(Sex = unique)),
    list(variables = list(Age = "all")),
    list(variables = list(Class = "sequential", Sex = "reference"))
  )
)

mod4 <- lm(Sepal.Length ~ Petal.Length * Petal.Width + Species, data = iris)
tidy_marginal_contrasts(mod4)
tidy_marginal_contrasts(
  mod4,
  variables_list = list(
    list(
      variables = list(Species = "sequential"),
      by = list(Petal.Length = c(2, 5))
    ),
    list(
      variables = list(Petal.Length = 2),
      by = list(Species = unique, Petal.Width = 2:4)
    )
  )
)

# Marginal Contrasts at the Mean
tidy_marginal_contrasts(mod, newdata = "mean")
tidy_marginal_contrasts(mod3, newdata = "mean")

Description

This function is deprecated. Use instead tidy_marginal_predictions() with the option newdata = "marginalmeans".

Usage

tidy_marginal_means(x, conf.int = TRUE, conf.level = 0.95, ...)

Arguments

Details

Use marginaleffects::marginal_means() to estimate marginal means and return a tibble tidied in a way that it could be used by broom.helpers functions. See marginaleffects::marginal_means()() for a list of supported models.

marginaleffects::marginal_means() estimate marginal means: adjusted predictions, averaged across a grid of categorical predictors, holding other numeric predictors at their means. Please refer to the documentation page of marginaleffects::marginal_means(). Marginal means are defined only for categorical variables.

For more information, see vignette("marginal_tidiers", "broom.helpers").

See Also

marginaleffects::marginal_means()

Other marginal_tieders: tidy_all_effects(), tidy_avg_comparisons(), tidy_avg_slopes(), tidy_ggpredict(), tidy_marginal_contrasts(), tidy_marginal_predictions(), tidy_margins()

Examples

# Average Marginal Means

df <- Titanic |>
  dplyr::as_tibble() |>
  tidyr::uncount(n) |>
  dplyr::mutate(Survived = factor(Survived, c("No", "Yes")))
mod <- glm(
  Survived ~ Class + Age + Sex,
  data = df, family = binomial
)
tidy_marginal_means(mod)
tidy_plus_plus(mod, tidy_fun = tidy_marginal_means)

mod2 <- lm(Petal.Length ~ poly(Petal.Width, 2) + Species, data = iris)
tidy_marginal_means(mod2)

Description

Use marginaleffects::avg_predictions() to estimate marginal predictions for each variable of a model and return a tibble tidied in a way that it could be used by broom.helpers functions. See marginaleffects::avg_predictions() for a list of supported models.

Usage

tidy_marginal_predictions(
  x,
  variables_list = "auto",
  conf.int = TRUE,
  conf.level = 0.95,
  ...
)

variables_to_predict(
  model,
  interactions = TRUE,
  categorical = unique,
  continuous = stats::fivenum
)

plot_marginal_predictions(x, variables_list = "auto", conf.level = 0.95, ...)