Identify the variable corresponding to each model coefficient
tidy_identify_variables() will add to the tidy tibble
three additional columns: variable, var_class, var_type and var_nlevels.
tidy_identify_variables(x, model = tidy_get_model(x), quiet = FALSE)
x |
a tidy tibble |
model |
the corresponding model, if not attached to |
quiet |
logical argument whether broom.helpers should not return a message when requested output cannot be generated. Default is FALSE |
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.
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_plus_plus(),
tidy_remove_intercept(),
tidy_select_variables()
Titanic %>%
dplyr::as_tibble() %>%
dplyr::mutate(Survived = factor(Survived, c("No", "Yes"))) %>%
glm(Survived ~ Class + Age * Sex, data = ., weights = .$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()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.