fsd.jb.test: Perform a Jarque-Bera Test on Normality of Functional Spatial...

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

Description

This function performs a test on normality of some functional spatial data.

Usage

fsd.jb.test(X.spca, Npc = NULL, L = NULL, var.method = "integral")

Arguments

X.spca	a spectral principal components analysis as performed by fsd.spca, i.e. a list that in particular contains the estimated SPC scores and the estimated spectral density.
Npc	the number of spectral principal components to use for the test.
L	the maximum lag to compute the covariance of the scores during the computation of the autocovariances.
var.method	the method that is used to calculate the long-run variance of the SFPC scores. Either "direct" or "integral".

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

T4	the test statistic.
p.value	the p-value.
df	the degrees of freedom.
T4.vector	the vector of the test statistics for each single SPC.

See Also

fsd.spca, fsd.spca.cov

Examples

## Not run: 
fsd.jb.test(F, L)

## End(Not run)

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