Poisson regression modelsSource:
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.
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
A single character string for the type of model. The only possible value for this model is "regression".
A non-negative number representing the total amount of regularization (
A number between zero and one (inclusive) giving the proportion of L1 regularization (i.e. lasso) in the model.
mixture = 1specifies a pure lasso model,
mixture = 0specifies a ridge regression model, and
0 < mixture < 1specifies an elastic net model, interpolating lasso and ridge.
A single character string specifying what computational engine to use for fitting.
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
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
captured as quosures. To pass values
programmatically, use the injection operator like so:
value <- 1 poisson_reg(argument = !!value)