stats::glm() uses maximum likelihood to fit a model for count data.
For this engine, there is a single mode: regression
This engine has no tuning parameters.
The poissonreg extension package is required to fit this model.
library(poissonreg)poisson_reg() %>%
set_engine("glm") %>%
translate()
## Poisson Regression Model Specification (regression)
##
## Computational engine: glm
##
## Model fit template:
## stats::glm(formula = missing_arg(), data = missing_arg(), weights = missing_arg(),
## family = stats::poisson)
Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via fit(), parsnip will
convert factor columns to indicators.
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on tidymodels.org.
The fit() and fit_xy() arguments have arguments called
case_weights that expect vectors of case weights.
This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on tidymodels.org.
The fit() and fit_xy() arguments have arguments called
case_weights that expect vectors of case weights.
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.”
If frequency weights are being used in your application, the
glm_grouped() model (and corresponding engine) may be
more appropriate.