SelectK

A list containing FPCA related subjects returned by MakeFPCAResults().

fpcaObj

A string or positive integer specifying selection criterion for number of functional principal components, available options: 'FVE', 'AIC', 'BIC', or the specified number of components - default: 'AIC'

criterion

A threshold percentage specified by user when using "FVE" as selection criterion: (0,1] - default: NULL

FVEthreshold

A list of <em>n</em> vectors containing the observed values for each individual - default: NULL

A list of <em>n</em> vectors containing the observation time points for each individual corresponding to y - default: NULL

Selects number of functional principal components for
given FPCA output and selection criteria

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".

SelectK: Selects number of functional principal components for given FPCA output and selection criteria

Description

Usage

Arguments

Value