Nothing
"glm.diag" <-
function (glmfit)
{
if (is.null(glmfit$prior.weights))
w <- rep(1, length(glmfit$residuals))
else w <- glmfit$prior.weights
sd = ifelse((class(glmfit) == "lm")[1],
summary(glmfit)$sigma, sqrt(summary(glmfit)$dispersion))
# sd <- sqrt(summary(glmfit)$dispersion)
dev <- residuals(glmfit, type = "deviance")/sd
pear <- residuals(glmfit, type = "pearson")/sd
h <- rep(0, length(w))
h[w != 0] <- lm.influence(glmfit)$hat
p <- glmfit$rank
rp <- pear/sqrt(1 - h)
rd <- dev/sqrt(1 - h)
cook <- (h * rp^2)/((1 - h) * p)
res <- sign(dev) * sqrt(dev^2 + h * rp^2)
list(res = res, rd = rd, rp = rp, cook = cook, h = h, sd = sd)
}
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