dpit_pois: Residuals for regression models with poisson outcomes

In assessor: Assessment Tools for Regression Models with Discrete and Semicontinuous Outcomes

Residuals for regression models with poisson outcomes

Description

Computes DPIT residuals for Poisson outcomes regression using the observed counts (y) and their corresponding fitted mean values (mu).

Usage

dpit_pois(y, mu)

Arguments

y	An observed outcome vector.
mu	A vector of fitted mean values.

Details

For formulation details on discrete outcomes, see dpit.

Value

A dpit object containing DPIT residuals.

Examples

## Poisson example
n <- 500
set.seed(1234)
# Covariates
x1 <- rnorm(n)
x2 <- rbinom(n, 1, 0.7)
# Coefficients
beta0 <- -2
beta1 <- 2
beta2 <- 1
lambda1 <- exp(beta0 + beta1 * x1 + beta2 * x2)
y <- rpois(n, lambda1)

# True model
poismodel <- glm(y ~ x1 + x2, family = poisson(link = "log"))
y1 <- poismodel$y
p1f <- fitted(poismodel)
dpit.poi <- dpit_pois(y=y1, mu=p1f)
resid.poi <- residuals(dpit.poi)
plot(dpit.poi)

assessor documentation built on Aug. 22, 2026, 9:06 a.m.