Lwls1D

Character holding the kernel type (see ?FPCA for supported kernels)

kernel_type

Vector of length N with measurement points

Vector of length N with measurement values

Vector of length M with output measurement points

xout

Scalar (integer) degree of polynomial fitted (default 1)

npoly

Scalar (integer) degree of derivative fitted (default 0)

nder

One dimensional local linear kernel smoother for longitudinal data.

Provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm or numerical integration. PACE is useful for the analysis of data that have been generated by a sample of underlying (but usually not fully observed) random trajectories. It does not rely on pre-smoothing of trajectories, which is problematic if functional data are sparsely sampled. PACE provides options for functional regression and correlation, for Longitudinal Data Analysis, the analysis of stochastic processes from samples of realized trajectories, and for the analysis of underlying dynamics. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".

Lwls1D: One dimensional local linear kernel smoother

Description

Usage

Arguments

Value