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.

## References

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