In tidymodels/parsnip: A Common API to Modeling and Analysis Functions

r descr_models("logistic_reg", "glm")

Tuning Parameters

This engine has no tuning parameters but you can set the family parameter (and/or link) as an engine argument (see below).

Translation from parsnip to the original package

logistic_reg() %>% 
  set_engine("glm") %>% 
  translate()

To use a non-default family and/or link, pass in as an argument to set_engine():

logistic_reg() %>% 
  set_engine("glm", family = stats::binomial(link = "probit")) %>% 
  translate()

Preprocessing requirements

Case weights

However, the documentation in [stats::glm()] assumes that is specific type of case weights are being used:"Non-NULL weights can be used to indicate that different observations have different dispersions (with the values in weights being inversely proportional to the dispersions); or equivalently, when the elements of weights are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations. For a binomial GLM prior weights are used to give the number of trials when the response is the proportion of successes: they would rarely be used for a Poisson GLM."

Saving fitted model objects

Examples

The "Fitting and Predicting with parsnip" article contains examples for logistic_reg() with the "glm" engine.

References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

tidymodels/parsnip documentation built on Feb. 19, 2025, 2:10 a.m.