assessor (version 1.1.1)

resid_disc: Residuals for regression models with discrete outcomes

Description

Calculates the DPIT residuals for regression models with discrete outcomes. Specifically, the model assumption of GLMs with binary, ordinal, Poisson, and negative binomial outcomes can be assessed using resid_disc().

Usage

resid_disc(model, plot=TRUE, scale="normal")

Value

DPIT residuals. If plot=TRUE, also produces a QQ plot.

Arguments

model

Model object (e.g., glm, glm.nb, polr)

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 scales. 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 defalut scale is normal.

Details

The DPIT residual for the \(i\)th observation is defined as follows: $$\hat{r}(Y_i|X_i) = \hat{G}\bigg(\hat{F}(Y_i|\mathbf{X}_i)\bigg)$$ where $$\hat{G}(s) = \frac{1}{n-1}\sum_{j=1, j \neq i}^{n}\hat{F}\bigg(\hat{F}^{(-1)}(\mathbf{X}_j)\bigg|\mathbf{X}_j\bigg)$$ and \(\hat{F}\) refers to the fitted cumulative distribution function. When scale="uniform", DPIT residuals should closely follow a uniform distribution, otherwise it implies model deficiency. When scale="normal", it applies the normal quantile transformation to the DPIT residuals $$\Phi^{-1}\left[\hat{r}(Y_i|\mathbf{X}_i)\right],i=1,\ldots,n.$$ The null pattern is the standard normal distribution in this case.

Check reference for more details.

References

Yang, Lu. "Double Probability Integral Transform Residuals for Regression Models with Discrete Outcomes." arXiv preprint arXiv:2308.15596 (2023).

Examples

library(MASS)
n <- 500
set.seed(1234)
## Negative Binomial example
# Covariates
x1 <- rnorm(n)
x2 <- rbinom(n, 1, 0.7)
### Parameters
beta0 <- -2
beta1 <- 2
beta2 <- 1
size1 <- 2
lambda1 <- exp(beta0 + beta1 * x1 + beta2 * x2)
# generate outcomes
y <- rnbinom(n, mu = lambda1, size = size1)

# True model
model1 <- glm.nb(y ~ x1 + x2)
resid.nb1 <- resid_disc(model1, plot = TRUE, scale = "uniform")

# Overdispersion
model2 <- glm(y ~ x1 + x2, family = poisson(link = "log"))
resid.nb2 <- resid_disc(model2, plot = TRUE, scale = "normal")

## 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"))
resid.bin1 <- resid_disc(model01, plot = TRUE)

# Missing covariates
model02 <- glm(y1 ~ x1, family = binomial(link = "logit"))
resid.bin2 <- resid_disc(model02, plot = TRUE)

## 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
poismodel1 <- glm(y ~ x1 + x2, family = poisson(link = "log"))
resid.poi1 <- resid_disc(poismodel1, plot = TRUE)

# Enlarge three outcomes
y <- rpois(n, lambda1) + c(rep(0, (n - 3)), c(10, 15, 20))
poismodel2 <- glm(y ~ x1 + x2, family = poisson(link = "log"))
resid.poi2 <- resid_disc(poismodel2, plot = TRUE)

## Ordinal example
n <- 500
set.seed(1234)
# Covariates
x1 <- rnorm(n, mean = 2)
# Coefficient
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")
resid.ord1 <- resid_disc(multimodel, plot = TRUE)

## Non-Proportionality
n <- 500
set.seed(1234)
x1 <- rnorm(n, mean = 2)
beta1 <- 3
beta2 <- 1
p0 <- plogis(1, location = beta1 * x1)
p1 <- plogis(4, location = beta2 * 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")
resid.ord2 <- resid_disc(multimodel, plot = TRUE)