Identify for each coefficient of a model the corresponding variable
It will also identify interaction terms and intercept(s).
model_identify_variables(model) ## Default S3 method: model_identify_variables(model) ## S3 method for class 'lavaan' model_identify_variables(model) ## S3 method for class 'aov' model_identify_variables(model) ## S3 method for class 'clm' model_identify_variables(model) ## S3 method for class 'clmm' model_identify_variables(model) ## S3 method for class 'gam' model_identify_variables(model)
model |
a model object |
A tibble with four columns:
term: coefficients of the model
variable: the corresponding variable
var_class: class of the variable (cf. stats::.MFclass())
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
Other model_helpers:
model_compute_terms_contributions(),
model_get_assign(),
model_get_coefficients_type(),
model_get_contrasts(),
model_get_model_frame(),
model_get_model_matrix(),
model_get_model(),
model_get_nlevels(),
model_get_n(),
model_get_offset(),
model_get_response(),
model_get_terms(),
model_get_weights(),
model_get_xlevels(),
model_list_contrasts(),
model_list_terms_levels(),
model_list_variables()
Titanic %>%
dplyr::as_tibble() %>%
dplyr::mutate(Survived = factor(Survived, c("No", "Yes"))) %>%
glm(
Survived ~ Class + Age * Sex,
data = ., weights = .$n,
family = binomial
) %>%
model_identify_variables()
iris %>%
lm(
Sepal.Length ~ poly(Sepal.Width, 2) + Species,
data = .,
contrasts = list(Species = contr.sum)
) %>%
model_identify_variables()Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.