| sim_reg5 {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 sampled from N(0,1). Outputs are generated following the equation
Y=X_{1}+0.707 X^{2}_{2} + 2\mathcal{1}_{(X_{3}>0)}+0.873 \log (X_{1})|X_{3}|+0.894 X_{2} X_{4}+2\mathcal{1}_{(X_{5}>0)}+0.464e^{X_{6}}+ \varepsilon, \varepsilon \sim \mathcal{N}(0,\sigma^{2})
Usage
sim_reg5(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.
Roy, M. H., & Larocque, D. (2012). Robustness of random forests for regression. Journal of Nonparametric Statistics, 24(4), 993-1006.
Examples
library(randomMachines)
sim_data <- sim_reg5(n=100)