Poisson regression models

poisson_reg() defines a generalized linear model for count data that follow a Poisson distribution. This function can fit regression models.

There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.

• glm¹²

• gee²

• glmer²

• glmnet²

• h2o²

• hurdle²

• stan²

• stan_glmer²

• zeroinfl²

¹ The default engine. ² Requires a parsnip extension package.

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

Usage

poisson_reg(
  mode = "regression",
  penalty = NULL,
  mixture = NULL,
  engine = "glm"
)

Arguments

mode

A single character string for the type of model. The only possible value for this model is "regression".

penalty

A non-negative number representing the total amount of regularization (glmnet only).

mixture

A number between zero and one (inclusive) giving the proportion of L1 regularization (i.e. lasso) in the model.

• mixture = 1 specifies a pure lasso model,

• mixture = 0 specifies a ridge regression model, and

• 0 < mixture < 1 specifies an elastic net model, interpolating lasso and ridge.

Available for glmnet and spark only.

engine

A single character string specifying what computational engine to use for fitting.

Details

This function only defines what type of model is being fit. Once an engine is specified, the method to fit the model is also defined. See set_engine() for more on setting the engine, including how to set engine arguments.

The model is not trained or fit until the fit() function is used with the data.

Each of the arguments in this function other than mode and engine are captured as quosures. To pass values programmatically, use the injection operator like so:

value <- 1
poisson_reg(argument = !!value)

References

https://www.tidymodels.org, Tidy Modeling with R, searchable table of parsnip models

See also

fit(), set_engine(), update(), glm engine details, gee engine details, glmer engine details, glmnet engine details, h2o engine details, hurdle engine details, stan engine details, stan_glmer engine details, zeroinfl engine details