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Poisson regression models with offsets

Source: R/poisson_reg_offset.R
poisson_reg_offset.Rd

poisson_reg_offset() defines a generalized linear model of count data with an offset that follows a Poisson distribution.

Usage

poisson_reg_offset(
  mode = "regression",
  penalty = NULL,
  mixture = NULL,
  engine = "glm_offset"
)

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.

Value

A model specification object with the classes poisson_reg_offset

and model_spec.

Details

This function is similar to parsnip::poisson_reg() except that specification of an offset column is required.

See also

parsnip::poisson_reg()

Examples

parsnip::show_model_info("poisson_reg_offset")
#> Information for `poisson_reg_offset`
#>  modes: unknown, regression 
#> 
#>  engines: 
#>    regression: glm_offset¹, glmnet_offset¹
#> 
#> ¹The model can use case weights.
#> 
#>  arguments: 
#>    glmnet_offset: 
#>       penalty --> lambda
#>       mixture --> alpha
#> 
#>  fit modules:
#>           engine       mode
#>       glm_offset regression
#>    glmnet_offset regression
#> 
#>  prediction modules:
#>          mode        engine      methods
#>    regression    glm_offset numeric, raw
#>    regression glmnet_offset numeric, raw
#> 

poisson_reg_offset()
#> Poisson Regression with Offsets Model Specification (regression)
#> 
#> Computational engine: glm_offset 
#>