The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

poissonreg

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).

Installation

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")

Available Engines

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

Example

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

Contributing

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.