sim.Friedman2-deprecated: 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)
Value
Returns a list with components
x
input values (independent variables)
y
output values (dependent variable)
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.
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.Friedman1-deprecated,
sim.Friedman3-deprecated