fsd.spca: Perform Spectral Principal Components Analysis on Spatial...

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

Description

This function performs spectral PCA on functional spatial data on a grid of dimension r.

Usage

fsd.spca(
  X,
  freq.res = 100,
  Npc = 3,
  L = 3,
  q = NULL,
  na.ignore = TRUE,
  return.F = FALSE,
  only.filters = FALSE
)

Arguments

X	the functional spatial data. Either an fd object or an array.
freq.res	the resolution for the computation of the spectral density.
Npc	the number of principal components to be computed.
L	the maximum lag for the filters. An integer or vector of integers.
q	a tuning parameter for the estimation of the the spectral density operator. An integer or vector of integers.
na.ignore	whether to ignore missing data points in the computation of the scores.
return.F	a boolean indicating whether to return the spectral density.
only.filters	a boolean indicating whether to compute only the filters, leaving out the scores.

Details

This function can be used to compute spectral PCA. By setting q = 0, it can also be used to perform static PCA.

Setting a higher value for freq.res increases the runtime significantly, but yields a more accurate result because of reduced integration errors.

Value

A list with components

F	the spectral density.
tuning.params	a list of the used tuning parameters freq.res, q and Lmax.
filters	the SPC filters.
scores	the SPC scores.
var	the theoretical fractions of variance explained by each PC.
X.mean	the mean of X.

See Also

fsd.spca.inverse, fsd.spectral.density

Examples

## Not run: 
fsd.spca(X)

## End(Not run)

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