depthbasedreconstructionPOFD: Depth-based reconstruction of partially observed functional...

In fdaPOIFD: Partially Observed Integrated Functional Depth

Depth-based reconstruction of partially observed functional data

Description

This function implements the reconstruction procedure [1] which is based on the depth measure [2] for partially observed functional data. Missing trajectories are imputed by the mean of the k nearest neighbors within the envelope. The parameter k is tuned minimizing the Mean Squared Error of the reconstruction in the observed part of the curve.

Usage

depthbasedreconstructionPOFD(data, id_recons = 1:dim(data)[2])

Arguments

data	Data matrix 'p' by 'n', being 'n' the number of functions and 'p' the number of grid points. The row names of the matrix should be the common evaluation grid and the column names the identifiers of each functional data.
id_recons	Vector indicating functions to be reconstructed. By default, all functions are reconstructed.

Details

[1] Elías, A., Jiménez, R., & Shang, H. L. (2023). Depth-based reconstruction method for incomplete functional data. Computational Statistics, 38(3), 1507-1535.

[2] Elías, A., Jiménez, R., Paganoni, A. M., & Sangalli, L. M. (2023). Integrated depths for partially observed functional data. Journal of Computational and Graphical Statistics, 32(2), 341-352.

Value

The reconstructed data matrix 'recons_data'.

Examples

data <- exampleData$PoFDintervals
recons_data <- depthbasedreconstructionPOFD(data, id_recons = 1:2)

fdaPOIFD documentation built on June 25, 2025, 5:10 p.m.