Residuals for regression models with poisson outcomes

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.

Value

A dpit object containing DPIT residuals.

Details

For formulation details on discrete outcomes, see dpit.

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)