VDPO (version 0.1.0)

data_generator_vd: Data generator function for the variable domain case

Description

Generates a variable domain functional regression model

Usage

data_generator_vd(
  N = 100,
  J = 100,
  nsims = 1,
  Rsq = 0.95,
  aligned = TRUE,
  multivariate = FALSE,
  beta_index = 1,
  use_x = FALSE,
  use_f = FALSE
)

Value

A list containing the following components:

• y: vector of length N containing the response variable.

• X_s: matrix of non-noisy functional data for the first functional covariate.

• X_se: matrix of noisy functional data for the first functional covariate

• Y_s: matrix of non-noisy functional data for the second functional covariate (if multivariate).

• Y_se: matrix of noisy functional data for the second covariate (if multivariate).

• x1: vector of length N containing the non-functional covariate (if use_x is TRUE).

• x2: vector of length N containing the observed values of the smooth term (if use_f is TRUE).

• smooth_term: vector of length N containing a smooth term (if use_f is TRUE).

• Beta: array containing the true functional coefficients.

Arguments

N

Number of subjects.

J

Number of maximum observations per subject.

nsims

Number of simulations per the simulation study.

Rsq

Variance of the model.

aligned

If the data that will be generated is aligned or not.

multivariate

If TRUE, the data is generated with 2 functional variables.

beta_index

Index for the beta.

use_x

If the data is generated with x.

use_f

If the data is generated with f.

Examples

# Basic usage with default parameters
sim_data <- data_generator_vd()

# Generate data with non-aligned domains
non_aligned_data <- data_generator_vd(N = 150, J = 120, aligned = FALSE)

# Generate multivariate functional data
multivariate_data <- data_generator_vd(N = 200, J = 100, multivariate = TRUE)

# Generate data with non-functional covariates and smooth term
complex_data <- data_generator_vd(
  N = 100,
  J = 150,
  use_x = TRUE,
  use_f = TRUE
)

# Generate data with a different beta function and R-squared value
custom_beta_data <- data_generator_vd(
  N = 80,
  J = 80,
  beta_index = 2,
  Rsq = 0.8
)

# Access components of the generated data
y <- sim_data$y # Response variable
X_s <- sim_data$X_s # Noise-free functional covariate
X_se <- sim_data$X_se # Noisy functional covariate