oneinfl: One-Inflated Regression Model

In oneinfl: Estimates OIPP and OIZTNB Regression Models

One-Inflated Regression Model

Description

Fits a one-inflated positive Poisson (OIPP) or one-inflated zero-truncated negative binomial (OIZTNB) regression model.

Usage

oneinfl(formula, df, dist = "negbin", start = NULL, method = "BFGS")

Arguments

formula	A symbolic description of the model to be fitted. Variables before the pipe | link to the usual Poisson rate parameter, after the pipe link to the one-inflation parameter.
df	A data frame containing the variables in the model.
dist	A character string specifying the distribution to use. Options are "Poisson" or "negbin".
start	Optional. A numeric vector of starting values for the optimization process. Defaults to NULL, in which case starting values are attempted to be chosen automatically.
method	A character string specifying the optimization method to be passed to optim. Defaults to "BFGS".

Details

This function fits a regression model for one-inflated counts. One-inflated models are used when there are an excess number of ones, relative to a Poisson or negative binomial process.

The function supports two distributions:

• "Poisson": One-inflated Poisson regression.

• "negbin": One-inflated negative binomial regression.

The function uses numerical optimization via optim to estimate the parameters.

Value

An object of class "oneinflmodel" containing the following components:

beta

Estimated coefficients for the rate component of the model.

gamma

Estimated coefficients for the one-inflation component of the model.

alpha

Dispersion parameter (only for negative binomial distribution).

vc

Variance-covariance matrix of the estimated parameters.

logl

Log-likelihood of the fitted model.

avgw

Average one-inflation probability.

absw

Mean absolute one-inflation probability.

dist

The distribution used for the model ("Poisson" or "negbin").

formula

The formula used for the model.

See Also

summary for summarizing the fitted model. margins for calculating the marginal effects of regressors. oneWald to test for no one-inflation. signifWald for testing the joint significance of a single regressor that appears before and after the pipe |. oneplot for plotting actual and predicted counts. predict for expected response/dependent variable at each observation. truncreg for fitting positive Poisson (PP) and zero-truncated negative binomial (ZTNB) models. oneLRT to test for no one-inflation or no overdispersion using a nested PP, OIPP, or ZTNB model.

Examples

# Example usage
df <- data.frame(x = rnorm(100), z = rnorm(100), y = rpois(100, lambda = 1) + 1)
model <- oneinfl(y ~ x | z, df = df, dist = "Poisson")
summary(model)
margins(model, df)
oneWald(model)
predict(model, df=df)

oneinfl documentation built on April 4, 2025, 12:05 a.m.