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Processes data by fitting a mean GAM model, extracting residuals, performing FPCA, and merging the results to create an enhanced dataset for functional regression analysis.

Usage

prepare_pupil_fpca(input_data, k_mean = 30, k_fpca = 15, example = "original")

Arguments

input_data

Raw pupil data

k_mean

Number of basis functions for mean model smooth terms (default: 30)

k_fpca

Number of knots for FPCA estimation (default: 15)

example

Choice for different model. If example = "original", will only include use as the only covariate. If example = "original", will include use, age and gender as covariates.

Value

A tibble containing:

  • Original pupil variables

  • FPCA eigenfunctions (Phi1, Phi2,...)

  • Sorted by ID and domain

Examples

if (requireNamespace("mgcv", quietly = TRUE)) {
data(pupil)
processed_data <- prepare_pupil_fpca(pupil)

processed_data <- prepare_pupil_fpca(pupil, k_mean = 5, k_fpca = 5)
}