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
The outputs are created according to the formula
where e is .
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)