In parsnip: A Common API to Modeling and Analysis Functions

r descr_models("poisson_reg", "hurdle")

Tuning Parameters

This engine has no tuning parameters.

Translation from parsnip to the underlying model call (regression)

r uses_extension("poisson_reg", "hurdle", "regression")

library(poissonreg)

poisson_reg() %>%
  set_engine("hurdle") %>%
  translate()

Preprocessing and special formulas for zero-inflated Poisson models

Specifying the statistical model details

For this particular model, a special formula is used to specify which columns affect the counts and which affect the model for the probability of zero counts. These sets of terms are separated by a bar. For example, y ~ x | z. This type of formula is not used by the base R infrastructure (e.g. model.matrix())

When fitting a parsnip model with this engine directly, the formula method is required and the formula is just passed through. For example:

library(tidymodels)

library(tidymodels)
tidymodels_prefer()

data("bioChemists", package = "pscl")
poisson_reg() %>% 
  set_engine("hurdle") %>% 
  fit(art ~ fem + mar | ment, data = bioChemists)

However, when using a workflow, the best approach is to avoid using [workflows::add_formula()] and use [workflows::add_variables()] in conjunction with a model formula:

data("bioChemists", package = "pscl")
spec <- 
  poisson_reg() %>% 
  set_engine("hurdle")

workflow() %>% 
  add_variables(outcomes = c(art), predictors = c(fem, mar, ment)) %>% 
  add_model(spec, formula = art ~ fem + mar | ment) %>% 
  fit(data = bioChemists) %>% 
  extract_fit_engine()