easystats | :milky_way: The R easystats-project | Data Visualization library

I'm facing a strange situation when building a function that wraps lm(). Specifically, I get an error that pertains to stats::model.frame() when passing the function's model output to effectsize::effectsize().

In the following example there are two scenarios, A and B. In A, I define a function that first builds a formula object my_formula <- as.formula(paste0(y, "~", x)) and then passes it to lm(). This function returns an object of class "lm". When I pass that object to effectsize::effectsize(), I get an error:

Error in stats::model.frame(formula = my_formula, data = data_std, drop.unused.levels = TRUE) : object 'my_formula' not found

Strangely, when I pass the same object to stats::model.frame(), it works.

In scenario B, I build a function in which the formula is specified within lm(), rather than a preemptive object. In that scenario, passing the output to effectsize() works.

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!