tidy_identify_variables {broom.helpers} | R Documentation |
Identify the variable corresponding to each model coefficient
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
x |
( |
model |
(a model object, e.g. |
quiet |
( |
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 wheretidy_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.
For fixest
models, a new column instrumental
is added to indicate
instrumental variables.
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_disambiguate_terms()
,
tidy_group_by()
,
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()