# NOT RUN {
## Generate some data.
set.seed(111)
n <- 500
## Regressors.
d <- data.frame(z = runif(n, -3, 3), w = runif(n, 0, 6))
## Response.
d$y <- with(d, 1.5 + cos(z) * sin(w) + rnorm(n, sd = 0.6))
# }
# NOT RUN {
## Estimate model.
b <- bamlss(y ~ te(z, w), data = d)
summary(b)
## Plot estimated effect.
plot(b, term = "te(z,w)", sliceplot = TRUE)
plot(b, term = "te(z,w)", sliceplot = TRUE, view = 2)
plot(b, term = "te(z,w)", sliceplot = TRUE, view = "w")
plot(b, term = "te(z,w)", sliceplot = TRUE, probs = seq(0, 1, length = 10))
# }
# NOT RUN {
## Variations.
d$f1 <- with(d, sin(z) * cos(w))
sliceplot(cbind(z = d$z, w = d$w, f1 = d$f1))
## Same with formula.
sliceplot(sin(z) * cos(w) ~ z + w, ylab = "f(z)", data = d)
## Compare with plot3d().
plot3d(sin(z) * 1.5 * w ~ z + w, zlab = "f(z,w)", data = d)
sliceplot(sin(z) * 1.5 * w ~ z + w, ylab = "f(z)", data = d)
sliceplot(sin(z) * 1.5 * w ~ z + w, view = 2, ylab = "f(z)", data = d)
# }
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