# NOT RUN {
## Generate some data.
set.seed(111)
n <- 500
## Regressor.
d <- data.frame(x = runif(n, -3, 3))
## Response.
d$y <- with(d, 10 + sin(x) + rnorm(n, sd = 0.6))
# }
# NOT RUN {
## Estimate model.
b <- bamlss(y ~ s(x), data = d)
summary(b)
## Plot estimated effect.
plot(b)
plot(b, rug = FALSE)
## Extract fitted values.
f <- fitted(b, model = "mu", term = "s(x)")
f <- cbind(d["x"], f)
## Now use plot2d.
plot2d(f)
plot2d(f, fill.select = c(0, 1, 0, 1))
plot2d(f, fill.select = c(0, 1, 0, 1), lty = c(2, 1, 2))
plot2d(f, fill.select = c(0, 1, 0, 1), lty = c(2, 1, 2),
scheme = 2)
## Variations.
plot2d(sin(x) ~ x, data = d)
d$f <- with(d, sin(d$x))
plot2d(f ~ x, data = d)
d$f1 <- with(d, f + 0.1)
d$f2 <- with(d, f - 0.1)
plot2d(f1 + f2 ~ x, data = d)
plot2d(f1 + f2 ~ x, data = d, fill.select = c(0, 1, 1), lty = 0)
plot2d(f1 + f2 ~ x, data = d, fill.select = c(0, 1, 1), lty = 0,
density = 20, poly.lty = 1, poly.lwd = 2)
plot2d(f1 + f + f2 ~ x, data = d, fill.select = c(0, 1, 0, 1),
lty = c(0, 1, 0), density = 20, poly.lty = 1, poly.lwd = 2)
# }
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