simulate_data: Simulate data through the function-on-function linear...

In slasso: S-LASSO Estimator for the Function-on-Function Linear Regression

Description

Generate synthetic data as in the simulation study of Centofanti et al. (2020).

Usage

simulate_data(scenario, n_obs = 3000, type_x = "Bspline")

Arguments

scenario	A character strings indicating the scenario considered. It could be "Scenario I", "Scenario II", "Scenario III", and "Scenario IV".
n_obs	Number of observations.
type_x	Covariate generating mechanism, either Bspline or Brownian.

Value

A list containing the following arguments:

X: Covariate matrix, where the rows correspond to argument values and columns to replications.

Y: Response matrix, where the rows correspond to argument values and columns to replications.

X_fd: Coavariate functions.

Y_fd: Response functions.

clus: True cluster membership vector.

References

Centofanti, F., Fontana, M., Lepore, A., & Vantini, S. (2020). Smooth LASSO Estimator for the Function-on-Function Linear Regression Model. arXiv preprint arXiv:2007.00529.

Examples

library(slasso)
data<-simulate_data("Scenario II",n_obs=150)

slasso documentation built on Oct. 15, 2021, 9:06 a.m.