fsd.spca.cov: Calculate the Covariance of the Spectral Principal Component...

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

Description

This function estimates the (theoretical) autocovariance for each spectral principal component score for some spatial functional data.

Usage

fsd.spca.cov(F, L = 3)

Arguments

F	the spectral density.
L	the maximum lag to compute the covariance of the scores

Details

To ensure accuracy of numerical integration during the Fourier transform, the frequencies of F should be a suitably dense grid.

Value

A list with components

laglist	the list of lags.
cov	a matrix with the autocovariances of the principal component scores.

See Also

fsd.spca, fsd.spca.var

Examples

## Not run: 
fsd.spca.cov(F, L)

## End(Not run)

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