data_generator_vd {VDPO} | R Documentation |
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
)
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. |
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 covariateY_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.
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