| sim_Friedman1 {bark} | R Documentation |
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 simulation functions:
sim_Friedman2(),
sim_Friedman3(),
sim_circle()
Other bark functions:
bark-package-deprecated,
bark-package,
bark(),
sim_Friedman2(),
sim_Friedman3(),
sim_circle()
Examples
sim_Friedman1(100, sd=1)