In parsnip: A Common API to Modeling and Analysis Functions

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r descr_models("linear_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

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linear_reg() |> 
  set_engine("glm") |> 
  translate()

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

#| label: glm-reg-engine
linear_reg() |> 
  set_engine("glm", family = stats::poisson(link = "sqrt")) |> 
  translate()

Preprocessing requirements

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Case weights

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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

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Examples

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

References

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

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