fee_negbin: First-Exposure Effect for One-Inflated Zero-Truncated...

In fee: Estimate the First-Exposure Effect (FEE) using Count Data Models

First-Exposure Effect for One-Inflated Zero-Truncated Negative Binomial Model

Description

Computes the first-exposure effect (FEE) for a model estimated using the one-inflated zero-truncated negative binomial distribution. The FEE represents the change in expected counts associated with the first exposure to treatment, relative to the counterfactual zero-truncated negative binomial model. This function is used internally by fee at given covariate values.

Usage

fee_negbin(b, g, a, X, Z)

Arguments

b	A numeric vector of estimated coefficients for the rate.
g	A numeric vector of estimated coefficients for inflation.
a	A dispersion parameter from the negative binomial model.
X	A numeric matrix of covariates for the rate.
Z	A numeric matrix of covariates for inflation.

Value

A numeric vector of first-exposure effect(s).

See Also

fee, fee_pois

fee documentation built on Aug. 8, 2025, 7:45 p.m.