dpit_ordi: Residuals for regression models with ordinal outcomes

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

Residuals for regression models with ordinal outcomes

Description

Computes DPIT residuals for regression models with ordinal outcomes using observed outcomes (y), ordinal outcome levels (level) and their fitted category probabilities (fitprob).

Usage

dpit_ordi(y, level, fitprob)

Arguments

y	An observed ordinal outcome vector.
level	The response levels in their ordinal order. For instance, c(0, 1, 2) or c("low", "medium", "high").
fitprob	A matrix of fitted category probabilities. Each row corresponds to an observation, and the columns must follow the order in level. Each row must sum to one.

Details

For formulation details on discrete outcomes, see dpit.

Value

A dpit object containing DPIT residuals.

Examples

## Ordinal example
library(MASS)
n <- 500
x1 <- rnorm(n, mean = 2)
beta1 <- 3
# True model
p0 <- plogis(1, location = beta1 * x1)
p1 <- plogis(4, location = beta1 * x1) - p0
p2 <- 1 - p0 - p1
genemult <- function(p) {
 rmultinom(1, size = 1, prob = c(p[1], p[2], p[3]))
}
test <- apply(cbind(p0, p1, p2), 1, genemult)
y1 <- rep(0, n)
y1[which(test[1, ] == 1)] <- 0
y1[which(test[2, ] == 1)] <- 1
y1[which(test[3, ] == 1)] <- 2
multimodel <- polr(as.factor(y1) ~ x1, method = "logistic")

y1 <- multimodel$model[,1]
lev1 <- multimodel$lev
fitprob1 <- fitted(multimodel)

dpit.ord <- dpit_ordi(y=y1, level=lev1, fitprob=fitprob1)
resid.ord <- residuals(dpit.ord)
plot(dpit.ord)

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