sim.Friedman1-deprecated: Simulated Regression Problem Friedman 1

In bark: Bayesian Additive Regression Kernels

Simulated Regression Problem Friedman 1

Description

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

## Not run: 
sim.Friedman1(100, sd=1)

## End(Not run)

bark documentation built on Oct. 6, 2024, 1:08 a.m.