Simulate data through the function-on-function linear regression model
Generate synthetic data as in the simulation study of Centofanti et al. (2020).
simulate_data(scenario, n_obs = 3000, type_x = "Bspline")
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.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.
library(slasso) data<-simulate_data("Scenario II",n_obs=150)
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.