In parsnip: A Common API to Modeling and Analysis Functions

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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")

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library(poissonreg)

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

Preprocessing and special formulas for zero-inflated Poisson models

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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:

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library(tidymodels)

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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()

The reason for this is that [workflows::add_formula()] will try to create the model matrix and either fail or create dummy variables prematurely.

Case weights

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parsnip documentation built on June 8, 2025, 12:10 p.m.