dpit_bin: Residuals for regression models with binary outcomes

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

Residuals for regression models with binary outcomes

Description

Computes DPIT residuals for regression models with binary outcomes using the observed responses (y) and their fitted distributional parameters (prob).

Usage

dpit_bin(y, prob)

Arguments

y	An observed outcome vector.
prob	A vector of fitted probabilities of one.

Details

For formulation details on discrete outcomes, see dpit_pois.

Value

A dpit object containing DPIT residuals.

Examples

## Binary example
n <- 500
set.seed(1234)
# Covariates
x1 <- rnorm(n, 1, 1)
x2 <- rbinom(n, 1, 0.7)
# Coefficients
beta0 <- -5
beta1 <- 2
beta2 <- 1
beta3 <- 3
q1 <- 1 / (1 + exp(beta0 + beta1 * x1 + beta2 * x2 + beta3 * x1 * x2))
y1 <- rbinom(n, size = 1, prob = 1 - q1)

# True model
model01 <- glm(y1 ~ x1 * x2, family = binomial(link = "logit"))
fitted1 <- fitted(model01)
y1 <- model01$y
dpit.bin1 <- dpit_bin(y=y1, prob=fitted1)
resid.bin1 <- residuals(dpit.bin1)
plot(dpit.bin1)

# Missing covariates
model02 <- glm(y1 ~ x1, family = binomial(link = "logit"))
y2 <- model02$y
fitted2 <- fitted(model02)
dpit.bin2 <- dpit_bin(y=y2, prob=fitted2)
resid.bin2 <- residuals(dpit.bin2)
plot(dpit.bin2)

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