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: 63.42
This 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.