Normalise sparse multivariate functional data
Normalise sparse functional sample given in an FPCA object
GetNormalisedSample(fpcaObj, errorSigma = FALSE) GetNormalizedSample(...)
fpcaObj |
An FPCA object. |
errorSigma |
Indicator to use sigma^2 error variance when normalising the data (default: FALSE) |
... |
Passed into GetNormalisedSample |
A list containing the normalised sample 'y' at times 't'
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
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)
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