Sparsify

A matrix of densely observed functional data, with each row containing one sample.

samp

A vector of grid points corresponding to the columns of <code>samp</code>.

A vector of integers. The number of observation per sample is chosen to be one of the elements in sparsity with equal chance.

sparsity

Sparsify in an "aggressive" manner making sure that near-by readings are excluded.

aggressive

Sparsify the observations into fragments, which are (almost) uniformly distributed in the time domain. Default to FALSE as not fragmenting. Otherwise a positive number specifying the approximate length of each fragment.

fragment

Given a matrix of densely observed functional data, make a sparsified sample.

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".

Sparsify: Sparsify densely observed functional data

Description

Usage

Arguments

Value