model_list_variables {broom.helpers}

List all the variables used in a model

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

model

a model object

labels

an optional named list or named vector of custom variable labels

only_variable

if TRUE, will return only "variable" column

add_var_type

if TRUE, add var_nlevels and var_type columns

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)) {
  Titanic %>%
    dplyr::as_tibble() %>%
    dplyr::mutate(Survived = factor(Survived, c("No", "Yes"))) %>%
    glm(
      Survived ~ Class + Age:Sex,
      data = ., weights = .$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()
}