```
# NOT RUN {
# Create some data first
n <- 20
set.seed(86) # For reproducibility of the random numbers
ldata <- data.frame(x2 = sort(runif(n)), y = sort(runif(n)))
library("splines") # To get ns() in R
# This will work for R 1.6.0 and later
fit <- lm(y ~ ns(x2, df = 5), data = ldata)
# }
# NOT RUN {
plot(y ~ x2, data = ldata)
lines(fitted(fit) ~ x2, data = ldata)
new.ldata <- data.frame(x2 = seq(0, 1, len = n))
points(predict(fit, new.ldata) ~ x2, new.ldata, type = "b", col = 2, err = -1)
# }
# NOT RUN {
# The following fails for R 1.6.x and later. It can be
# made to work with smart prediction provided
# ns is changed to sm.ns and scale is changed to sm.scale:
fit1 <- lm(y ~ ns(scale(x2), df = 5), data = ldata)
# }
# NOT RUN {
plot(y ~ x2, data = ldata, main = "Safe prediction fails")
lines(fitted(fit1) ~ x2, data = ldata)
points(predict(fit1, new.ldata) ~ x2, new.ldata, type = "b", col = 2, err = -1)
# }
# NOT RUN {
# Fit the above using smart prediction
# }
# NOT RUN {
library("VGAM") # The following requires the VGAM package to be loaded
fit2 <- vglm(y ~ sm.ns(sm.scale(x2), df = 5), uninormal, data = ldata)
fit2@smart.prediction
plot(y ~ x2, data = ldata, main = "Smart prediction")
lines(fitted(fit2) ~ x2, data = ldata)
points(predict(fit2, new.ldata, type = "response") ~ x2, data = new.ldata,
type = "b", col = 2, err = -1)
# }
```

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