Lwls2D

A scalar or a vector of length 2 specifying the bandwidth.

Kernel used: 'gauss', 'rect', 'gausvar', 'epan' (default), 'quar'.

kern

An n by 2 dataframe or matrix of x-coordinate.

a p1-vector of first output coordinate grid. Defaults to the input gridpoints if left unspecified.

xout1

a p2-vector of second output coordinate grid. Defaults to the input gridpoints if left unspecified.

xout2

alternative to xout1 and xout2. A matrix of p by 2 specifying the output points (may be inefficient if the size of <code>xout</code> is small).

xout

a vector with the indeces of x-/y-/w-in to be used (Default: NULL)

subset

using function for cross-covariance estimation (Default: FALSE)

crosscov

Two dimensional local weighted least squares smoother. Only local linear smoother for estimating the original curve is available (no higher order, no derivative).

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".

Lwls2D: Two dimensional local linear kernel smoother.

Description

Usage

Arguments

Value