GetNormalisedSample: Normalise sparse multivariate functional data

In fdapace: Functional Data Analysis and Empirical Dynamics

Normalise sparse multivariate functional data

Description

Normalise sparse functional sample given in an FPCA object

Usage

GetNormalisedSample(fpcaObj, errorSigma = FALSE)

GetNormalizedSample(...)

Arguments

fpcaObj	An FPCA object.
errorSigma	Indicator to use sigma^2 error variance when normalising the data (default: FALSE)
...	Passed into GetNormalisedSample

Value

A list containing the normalised sample 'y' at times 't'

References

Chiou, Jeng-Min and Chen, Yu-Ting and Yang, Ya-Fang. "Multivariate Functional Principal Component Analysis: A Normalization Approach" Statistica Sinica 24 (2014): 1571-1596

Examples

set.seed(1)
n <- 100
M <- 51
pts <- seq(0, 1, length.out=M)
mu <- rep(0, length(pts))
sampDense <- MakeGPFunctionalData(n, M, mu, K=1, basisType='sin', sigma=0.01)
samp4 <- MakeFPCAInputs(tVec=sampDense$pts, yVec=sampDense$Yn)
res4E <- FPCA(samp4$Ly, samp4$Lt, list(error=TRUE))
sampN <- GetNormalisedSample(res4E, errorSigma=TRUE)

CreatePathPlot(subset=1:20, inputData=samp4, obsOnly=TRUE, showObs=FALSE)
CreatePathPlot(subset=1:20, inputData=sampN, obsOnly=TRUE, showObs=FALSE)

fdapace documentation built on July 3, 2024, 5:08 p.m.