Boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data. The current relase version can be found on CRAN (http://cran.r-project.org/package=mboost).
As @hofnerb pointed out that bbs() with argument constraint should not be used for decreasing or increasing effects, this argument should be deprecated.
Instead bmono() can be used.
Consider an example where the true function is not monotonic:
library(mboost)
x <- sort(rnorm(100))
y <- x^2
y <- y - mean(y)
dat <- data.frame(y = y, x = x)
m <- mboost(y ~ bbs(x), data = dat)
m_bbs <- mboost(y ~ bbs(x, constraint = "increasing"), data = dat)
m_bmono <- mboost(y ~ bmono(x, constraint = "increasing"), data = dat)
par(mfrow = c(1,3))
plot(m, main = "bbs(x)"); lines(x, y, col=2)
plot(m_bbs, main = "bbs(x, constraint = \"increasing\")"); lines(x, y, col=2)
plot(m_bmono, main = "bmono(x, constraint = \"increasing\")"); lines(x, y, col=2)
As @hofnerb pointed out that
bbs()
with argumentconstraint
should not be used for decreasing or increasing effects, this argument should be deprecated. Insteadbmono()
can be used.Consider an example where the true function is not monotonic:
@Almond-S