Lets say I create an lm model with 2 predictors at least one which is categorical and there is also an interaction term involved. Now I would like to also get the significance level for the overall interaction term. for this I would simply use a type 3 Anova
in this example I want to get the significance level for Petal.Length:Species
lm(Sepal.Length ~ Petal.Length + Species + Petal.Length:Species, data = iris) %>%
car::Anova(type = 3)
However, this approach doesn't seem to work for parsnip models:
rec <-
recipe(Sepal.Length ~ Petal.Length + Species, data = iris) %>%
step_interact(terms = ~ Species:Petal.Length)
lm_spec <-
linear_reg() %>%
set_engine("lm")
wf_2p_int <-
workflow() %>%
add_recipe(rec) %>%
add_model(lm_spec) %>%
fit(iris)
wf_2p_int %>%
extract_fit_engine() %>%
car::Anova(type = 3)
Is there any straight-forward way to get the same results for models created via the tidymodels approach?