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, plot=TRUE, scale="normal", line_args=list(), ...)

Arguments

y	An observed outcome vector.
prob	A vector of fitted probabilities of one.
plot	A logical value indicating whether or not to return QQ-plot
scale	You can choose the scale of the residuals among normal and uniform. The sample quantiles of the residuals are plotted against the theoretical quantiles of a standard normal distribution under the normal scale, and against the theoretical quantiles of a uniform (0,1) distribution under the uniform scale. The default scale is normal.
line_args	A named list of graphical parameters passed to graphics::abline() to modify the reference (red) 45° line in the QQ plot. If left empty, a default red dashed line is drawn.
...	Additional graphical arguments passed to stats::qqplot() for customizing the QQ plot (e.g., pch, col, cex, xlab, ylab).

Details

For formulation details on discrete outcomes, see dpit_pois.

Value

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
resid.bin1 <- dpit_bin(y=y1, prob=fitted1)

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

assessor documentation built on March 23, 2026, 1:06 a.m.