# NOT RUN {
nn <- 2000
mydat <- data.frame(x = seq(0, 1, length = nn))
mydat <- transform(mydat, mu = loglink(-0 + 1.5*x + 0.2*x^2, inverse = TRUE))
mydat <- transform(mydat, mu = loglink(0 - sin(8*x), inverse = TRUE))
mydat <- transform(mydat, y = rexp(nn, rate = 1/mu))
(fit <- vgam(y ~ s(x,df = 5), amlexponential(w = c(0.001, 0.1, 0.5, 5, 60)),
mydat, trace = TRUE))
fit@extra
# }
# NOT RUN {
# These plots are against the sqrt scale (to increase clarity)
par(mfrow = c(1,2))
# Quantile plot
with(mydat, plot(x, sqrt(y), col = "blue", las = 1, main =
paste(paste(round(fit@extra$percentile, digits = 1), collapse = ", "),
"percentile-expectile curves")))
with(mydat, matlines(x, sqrt(fitted(fit)), lwd = 2, col = "blue", lty = 1))
# Compare the fitted expectiles with the quantiles
with(mydat, plot(x, sqrt(y), col = "blue", las = 1, main =
paste(paste(round(fit@extra$percentile, digits = 1), collapse = ", "),
"percentile curves are orange")))
with(mydat, matlines(x, sqrt(fitted(fit)), lwd = 2, col = "blue", lty = 1))
for (ii in fit@extra$percentile)
with(mydat, matlines(x, sqrt(qexp(p = ii/100, rate = 1/mu)), col = "orange"))
# }
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