poissonreg enables the parsnip package to fit various types of Poisson regression models including ordinary generalized linear models, simple Bayesian models (via rstanarm), and two zero-inflated Poisson models (via pscl).
You can install the released version of poissonreg from CRAN with:
install.packages("poissonreg")Install the development version from GitHub with:
require("devtools")
install_github("tidymodels/poissonreg")The poissonreg package provides engines for the models in the following table.
| model | engine | mode |
|---|---|---|
| poisson_reg | glm | regression |
| poisson_reg | hurdle | regression |
| poisson_reg | zeroinfl | regression |
| poisson_reg | glmnet | regression |
| poisson_reg | stan | regression |
A log-linear model for categorical data analysis:
library(poissonreg)
# 3D contingency table from Agresti (2007):
poisson_reg() %>%
set_engine("glm") %>%
fit(count ~ (.)^2, data = seniors)
#> parsnip model object
#>
#>
#> Call: stats::glm(formula = count ~ (.)^2, family = stats::poisson,
#> data = data)
#>
#> Coefficients:
#> (Intercept) marijuanayes
#> 5.6334 -5.3090
#> cigaretteyes alcoholyes
#> -1.8867 0.4877
#> marijuanayes:cigaretteyes marijuanayes:alcoholyes
#> 2.8479 2.9860
#> cigaretteyes:alcoholyes
#> 2.0545
#>
#> Degrees of Freedom: 7 Total (i.e. Null); 1 Residual
#> Null Deviance: 2851
#> Residual Deviance: 0.374 AIC: 63.42This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
-
For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
-
If you think you have encountered a bug, please submit an issue.
-
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
-
Check out further details on contributing guidelines for tidymodels packages and how to get help.