# quantile residuals for linear regression
fm <- ols(stack.loss ~ ., data = stackloss)
res <- rquantile(fm)
plot(res, ylim = c(-3,3), ylab = "quantile residuals")
abline(h = 0, lwd = 2, col = "gray75")
abline(h = c(-2,2), lwd = 2, lty = 2, col = "red")
text(21, res[21], as.character(21), pos = 1)
# quantile residuals for LAD regression
data(ereturns)
fm <- lad(m.marietta ~ CRSP, data = ereturns)
res <- rquantile(fm)
plot(res, ylim = c(-2,4.5), ylab = "quantile residuals")
abline(h = 0, lwd = 2, col = "gray75")
abline(h = c(-2,2), lwd = 2, lty = 2, col = "red")
obs <- c(8,15,34)
text(obs, res[obs], as.character(obs), pos = 3)
# quantile residuals for ridge regression
data(portland)
fm <- ridge(y ~ ., data = portland)
res <- rquantile(fm)
plot(res, ylim = c(-2,2), ylab = "quantile residuals")
abline(h = 0, lwd = 2, col = "gray75")
# quantile residuals for nonlinear regression
data(skeena)
model <- recruits ~ b1 * spawners * exp(-b2 * spawners)
fm <- nls(model, data = skeena, start = list(b1 = 3, b2 = 0))
res <- rquantile(fm)
plot(res, ylim = c(-3,3), ylab = "quantile residuals")
abline(h = 0, lwd = 2, col = "gray75")
abline(h = c(-2,2), lwd = 2, lty = 2, col = "red")
text(5, res[5], as.character(5), pos = 3)
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