Simulated Regression Problem Friedman 1

The regression problem Friedman 1 as described in Friedman (1991) and Breiman (1996). Inputs are 10 independent variables uniformly distributed on the interval \([0,1]\), only 5 out of these 10 are actually used. Outputs are created according to the formula $$y = 10 \sin(\pi x1 x2) + 20 (x3 - 0.5)^2 + 10 x4 + 5 x5 + e$$ where e is \(N(0,sd^2)\).

Usage

sim.Friedman1(n, sd=1)

Arguments

n

number of data points to create

sd

standard deviation of noise, with default value 1

Value

Returns a list with components

x

input values (independent variables)

y

output values (dependent variable)

References

Breiman, Leo (1996) Bagging predictors. Machine Learning 24, pages 123-140.
Friedman, Jerome H. (1991) Multivariate adaptive regression splines. The Annals of Statistics 19 (1), pages 1-67.

See also

Other bark deprecated functions: bark-deprecated, bark-package-deprecated, sim.Circle-deprecated, sim.Friedman2-deprecated, sim.Friedman3-deprecated

Examples

if (FALSE) { # \dontrun{
sim.Friedman1(100, sd=1)
} # }