roipp: Generate Random Counts from a One-Inflated Poisson Process

In oneinfl: Estimates OIPP and OIZTNB Regression Models

Generate Random Counts from a One-Inflated Poisson Process

Description

Simulates count data from a one-inflated Poisson process using specified parameters for the rate and one-inflation components.

Usage

roipp(b, g, X, Z)

Arguments

b	A numeric vector of coefficients for the rate component.
g	A numeric vector of coefficients for the one-inflation component.
X	A matrix or data frame of predictor variables for the rate component.
Z	A matrix or data frame of predictor variables for the one-inflation component.

Details

This function generates count data from a one-inflated Poisson process. The process combines:

• A Poisson distribution for counts greater than one.

• A one-inflation component that adjusts the probability of observing a count of one.

The algorithm:

1. Calculates the rate parameter (\lambda) as \exp(X \cdot b).

2. Computes the one-inflation probabilities (\omega) based on Z \cdot g.

3. Simulates counts for each observation:

   • Draws a random number to determine whether the count is one.

   • Iteratively calculates probabilities for higher counts until the random number is matched.

This function is useful for generating synthetic data for testing or simulation studies involving one-inflated Poisson models.

Value

A numeric vector of simulated count data.

See Also

oneinfl for fitting one-inflated models.

Examples

# Example usage
set.seed(123)
X <- matrix(rnorm(100), ncol = 2)
Z <- matrix(rnorm(100), ncol = 2)
b <- c(0.5, -0.2)
g <- c(1.0, 0.3)
simulated_data <- roipp(b, g, X, Z)
print(simulated_data)

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