randomMachines (version 0.1.1)

sim_reg4: Simulation for a regression toy examples from Random Machines Regression 3

Description

Simulation toy example initially found in Van der Laan, et.al (2016), and used and escribed by Ara et. al (2022). Inputs are 6 independent variables uniformly distributed on the interval \([-1,1]\). Outputs are generated following the equation $$Y={X^{2}_{1}}+{X}^{2}_{2}{X_{3}}e^{-|{X_{4}}|}+{X_{6}}-{X_{5}}+ \varepsilon, \varepsilon \sim \mathcal{N}(0,\sigma^{2})$$

Usage

sim_reg4(n, sigma)

Value

A simulated data.frame with two predictors and the target variable.

Arguments

n

Sample size

sigma

Standard deviation of residual noise

Author

Mateus Maia: , Anderson Ara:

References

Ara, Anderson, et al. "Regression random machines: An ensemble support vector regression model with free kernel choice." Expert Systems with Applications 202 (2022): 117107.

Van der Laan, M. J., Polley, E. C., & Hubbard, A. E. (2007). Super learner. Statistical applications in genetics and molecular biology, 6(1).

Examples

library(randomMachines)
sim_data <- sim_reg4(n=100)