oneinfl (version 1.0.2)

truncreg: Truncated Regression Model

Description

Fits a positive Poisson (PP) or zero-truncated negative binomial (ZTNB) regression model.

Usage

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

Value

An object of class `"truncmodel"` containing the following components:

beta

Estimated coefficients for the regression model.

alpha

Dispersion parameter (only for negative binomial distribution).

vc

Variance-covariance matrix of the estimated parameters.

logl

Log-likelihood of the fitted model.

dist

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

formula

The formula used for the model.

Arguments

formula

A symbolic description of the model to be fitted.

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 zero-truncated counts. Zero-truncated models are used when the count data does not include zeros, such as in cases where only positive counts are observed.

The function supports two distributions: - `"Poisson"`: Zero-truncated Poisson regression. - `"negbin"`: Zero-truncated negative binomial regression.

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

See Also

summary for summarizing the fitted model.

Examples

# Example usage
df <- data.frame(x = rnorm(100), y = rpois(100, lambda = 1) + 1)
model <- truncreg(y ~ x, df = df, dist = "Poisson")
summary(model)