Extract plain model from tidymodel object

Question

Is it possible to extract, say, a model of class glm from a tidymodel built with recipe and logistic_reg() %>% set_engine("glm")?

I'd like to use packages from the easystats project, which require "normal", non-tidy models. The workflow extractor function (pull_workflow_fit()) returns an object of class `"_glm" "model_fit", which doesn't seem to be compatible.

I understand I can generate a model using glm() and the same formula as in the recipe, but it seems to me the fitted parameters differ. Thanks!

Answer 1

The easystats package suite supports tidymodels since the last updates:

library(tidymodels)

data(two_class_dat)

glm_spec <- logistic_reg() %>%
  set_engine("glm") 

norm_rec <- recipe(Class ~ A + B, data = two_class_dat) %>%
  step_normalize(all_predictors())

workflow() %>%
  add_recipe(norm_rec) %>%
  add_model(glm_spec) %>%
  fit(two_class_dat) %>%
  pull_workflow_fit() %>% 
  parameters::model_parameters()
#> Parameter   | Log-Odds |   SE |         95% CI |     z |      p
#> ---------------------------------------------------------------
#> (Intercept) |    -0.35 | 0.10 | [-0.54, -0.16] | -3.55 | < .001
#> A           |    -1.11 | 0.17 | [-1.44, -0.79] | -6.64 | < .001
#> B           |     2.80 | 0.21 | [ 2.40,  3.22] | 13.33 | < .001


workflow() %>%
  add_recipe(norm_rec) %>%
  add_model(glm_spec) %>%
  fit(two_class_dat) %>%
  pull_workflow_fit() %>% 
  parameters::model_parameters() %>% 
  plot()

workflow() %>%
  add_recipe(norm_rec) %>%
  add_model(glm_spec) %>%
  fit(two_class_dat) %>%
  pull_workflow_fit() %>% 
  parameters::model_parameters() %>% 
  parameters::print_md()

Parameter	Log-Odds	SE	95% CI	z	p
(Intercept)	-0.35	0.10	(-0.54, -0.16)	-3.55	< .001
A	-1.11	0.17	(-1.44, -0.79)	-6.64	< .001
B	2.80	0.21	(2.40, 3.22)	13.33	< .001

workflow() %>%
  add_recipe(norm_rec) %>%
  add_model(glm_spec) %>%
  fit(two_class_dat) %>%
  pull_workflow_fit() %>% 
  performance::model_performance()
#> # Indices of model performance
#> 
#> AIC     |     BIC | Tjur's R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical |   PCP
#> ----------------------------------------------------------------------------------------------
#> 679.950 | 693.970 |     0.460 | 0.362 | 0.925 |    0.426 |      -Inf |           0.003 | 0.733

^{Created on 2021-04-25 by the reprex package (v2.0.0)}