fsd.spca.filters: Calculate the Spectral Principal Components Filters Spatial...

In kuenzer/fsd: Functional Spatial Data: Spatial Functional PCA

Description

This function calculates the SPC filters from a given spectral density operator.

Usage

fsd.spca.filters(F, Npc = 1, L = 3)

Arguments

F	the spectral density.
Npc	the number of principal components to be computed.
L	the maximum lag for the filters, as an integer or vector of integers.

Details

The eigenvectors used for the calculation of the Fourier expansion are oriented such that the sum of their coordinates over the basis of the fd object (i.e. the entries in the coefs array) is a non-negative real number. If the basis is not orthonormal, this is done with respect to a virtual orthonormal basis in the background.

Value

the SPC filters.

See Also

fsd.spca

Examples

## Not run: 
fsd.spca.filters(F)

## End(Not run)

kuenzer/fsd documentation built on July 21, 2020, 1:57 p.m.