Computes DPIT residuals for Poisson outcomes regression using the observed counts (y) and their
corresponding fitted mean values (mu).
Usage
dpit_pois(y, mu, plot=TRUE, scale="normal", line_args=list(), ...)Arguments
- y
An observed outcome vector.
- mu
A vector of fitted mean values.
- plot
A logical value indicating whether or not to return QQ-plot
- scale
You can choose the scale of the residuals among
normalanduniform. 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 isnormal.- 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.
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)
resid.poi <- dpit_pois(y=y1, mu=p1f)
