Including variables used only in an interaction.
Usage
model_list_variables(
model,
labels = NULL,
only_variable = FALSE,
add_var_type = FALSE,
instrumental_suffix = " (instrumental)"
)
# Default S3 method
model_list_variables(
model,
labels = NULL,
only_variable = FALSE,
add_var_type = FALSE,
instrumental_suffix = " (instrumental)"
)
# S3 method for class 'lavaan'
model_list_variables(
model,
labels = NULL,
only_variable = FALSE,
add_var_type = FALSE,
instrumental_suffix = " (instrumental)"
)
# S3 method for class 'logitr'
model_list_variables(
model,
labels = NULL,
only_variable = FALSE,
add_var_type = FALSE,
instrumental_suffix = " (instrumental)"
)
Arguments
- model
(a model object, e.g.
glm
)
A model object.- labels
(
list
orstring
)
An optional named list or named vector of custom variable labels.- only_variable
(
logical
)
IfTRUE
, will return only "variable" column.- add_var_type
(
logical
)
IfTRUE
, addvar_nlevels
andvar_type
columns.- instrumental_suffix
(
string
)
Suffix added to variable labels for instrumental variables (fixest
models).NULL
to add nothing.
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
# \donttest{
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()
#> # A tibble: 6 × 4
#> variable var_class label_attr var_label
#> <chr> <chr> <chr> <chr>
#> 1 Survived factor NA Survived
#> 2 Class character NA Class
#> 3 Age character NA Age
#> 4 Sex character NA Sex
#> 5 (weights) numeric NA (weights)
#> 6 Age:Sex NA NA Age:Sex
lm(
Sepal.Length ~ poly(Sepal.Width, 2) + Species,
data = iris,
contrasts = list(Species = contr.sum)
) |>
model_list_variables()
#> # A tibble: 3 × 4
#> variable var_class label_attr var_label
#> <chr> <chr> <chr> <chr>
#> 1 Sepal.Length numeric NA Sepal.Length
#> 2 Sepal.Width nmatrix.2 NA Sepal.Width
#> 3 Species factor NA Species
glm(
response ~ poly(age, 3) + stage + grade * trt,
na.omit(gtsummary::trial),
family = binomial,
) |>
model_list_variables()
#> # A tibble: 6 × 4
#> variable var_class label_attr var_label
#> <chr> <chr> <chr> <chr>
#> 1 response integer Tumor Response Tumor Response
#> 2 age nmatrix.3 NA age
#> 3 stage factor T Stage T Stage
#> 4 grade factor Grade Grade
#> 5 trt character Chemotherapy Treatment Chemotherapy Treatment
#> 6 grade:trt NA NA grade:trt
# }