| sim_reg4 {randomMachines} | R Documentation |
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)
Arguments
n |
Sample size |
sigma |
Standard deviation of residual noise |
Value
A simulated data.frame with two predictors and the target variable.
Author(s)
Mateus Maia: mateusmaia11@gmail.com, Anderson Ara: ara@ufpr.br
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)