# NOT RUN {
# Example 1: Simulated data
gdata <- data.frame(y1 = rgumbel(n = 1000, loc = 100, scale = exp(1)))
fit1 <- vglm(y1 ~ 1, gumbelff(perc = NULL), data = gdata, trace = TRUE)
coef(fit1, matrix = TRUE)
Coef(fit1)
head(fitted(fit1))
with(gdata, mean(y1))
# Example 2: Venice data
(fit2 <- vglm(cbind(r1, r2, r3, r4, r5) ~ year, data = venice,
gumbel(R = 365, mpv = TRUE), trace = TRUE))
head(fitted(fit2))
coef(fit2, matrix = TRUE)
sqrt(diag(vcov(summary(fit2)))) # Standard errors
# Example 3: Try a nonparametric fit ---------------------
# Use the entire data set, including missing values
# Same as as.matrix(venice[, paste0("r", 1:10)]):
Y <- Select(venice, "r", sort = FALSE)
fit3 <- vgam(Y ~ s(year, df = 3), gumbel(R = 365, mpv = TRUE),
data = venice, trace = TRUE, na.action = na.pass)
depvar(fit3)[4:5, ] # NAs used to pad the matrix
# }
# NOT RUN {
# Plot the component functions
par(mfrow = c(2, 3), mar = c(6, 4, 1, 2) + 0.3, xpd = TRUE)
plot(fit3, se = TRUE, lcol = "blue", scol = "limegreen", lty = 1,
lwd = 2, slwd = 2, slty = "dashed")
# Quantile plot --- plots all the fitted values
qtplot(fit3, mpv = TRUE, lcol = c(1, 2, 5), tcol = c(1, 2, 5), lwd = 2,
pcol = "blue", tadj = 0.1, ylab = "Sea level (cm)")
# Plot the 99 percentile only
year <- venice[["year"]]
matplot(year, Y, ylab = "Sea level (cm)", type = "n")
matpoints(year, Y, pch = "*", col = "blue")
lines(year, fitted(fit3)[, "99%"], lwd = 2, col = "orange")
# Check the 99 percentiles with a smoothing spline.
# Nb. (1-0.99) * 365 = 3.65 is approx. 4, meaning the 4th order
# statistic is approximately the 99 percentile.
plot(year, Y[, 4], ylab = "Sea level (cm)", type = "n",
main = "Orange is 99 percentile, Green is a smoothing spline")
points(year, Y[, 4], pch = "4", col = "blue")
lines(year, fitted(fit3)[, "99%"], lty = 1, col = "orange")
lines(smooth.spline(year, Y[, 4], df = 4), col = "limegreen", lty = 2)
# }
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