| sim_Friedman3 {bark} | R Documentation |
Simulated Regression Problem Friedman 3
Description
The regression problem Friedman 3 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
\mbox{atan}((x2 x3 - (1/(x2 x4)))/x1) + e
where e is N(0,sd^2).
Usage
sim_Friedman3(n, sd = 0.1)
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.
See Also
Other bark simulation functions:
sim_Friedman1(),
sim_Friedman2(),
sim_circle()
Other bark functions:
bark-package-deprecated,
bark-package,
bark(),
sim_Friedman1(),
sim_Friedman2(),
sim_circle()
Examples
sim_Friedman3(n=100, sd=0.1)