sim_Friedman2 {bark} R Documentation

## Simulated Regression Problem Friedman 2

### Description

The regression problem Friedman 2 as described in Friedman (1991) and Breiman (1996). Inputs are 4 independent variables uniformly distributed over the ranges

0 \le x1 \le 100

40 \pi \le x2 \le 560 \pi

0 \le x3 \le 1

1 \le x4 \le 11

The outputs are created according to the formula

y = (x1^2 + (x2 x3 - (1/(x2 x4)))^2)^{0.5} + e

where e is N(0,sd^2).

### Usage

sim_Friedman2(n, sd = 125)


### Arguments

 n number of data points to create sd Standard deviation of noise. The default value of 125 gives a signal to noise ratio (i.e., the ratio of the standard deviations) of 3:1. Thus, the variance of the function itself (without noise) accounts for 90% of the total variance.

### 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.

Other bark simulation functions: sim_Friedman1(), sim_Friedman3(), sim_circle()
Other bark functions: bark-package-deprecated, bark-package, bark(), sim_Friedman1(), sim_Friedman3(), sim_circle()
sim_Friedman2(100, sd=125)