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
)
Value
A tibble with three columns:
variable
: the corresponding variablevar_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, otherwisevariable
)
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 (FALSE) { # interactive()
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()
}
}