mfpca: Functional principal component analysis for univariate or...

In funHDDC: Univariate and Multivariate Model-Based Clustering in Group-Specific Functional Subspaces

Functional principal component analysis for univariate or multivariate functional data

Description

It provides functional principal component analysis for univariate or multivariate functional data.

Usage

  mfpca(fdobj,center)

Arguments

fdobj	For univariate FPCA: a functional data object produced by fd() function of fda package, for multivariate FPCA: a list of functional data objects.
center	If TRUE (default), it centers each lines of data coefficients by the mean before calculating the FPCA.

Value

eigval	A list of eigen values.
harmonics	A functional data object for the harmonics or eigenfunctions.
scores	A matrix of scores on the harmonics.
varprop	A vector giving the proportion of variance explained by each harmonic.
meanfd	A functional data object giving the mean function after centering (default) or the mean function of raw data.

Examples

  ####Univariate case: "Canadian temperature" data (Ramsey & Silverman)
  daybasis65 <- create.fourier.basis(c(0, 365), nbasis=65, period=365)
  daytempfd <- smooth.basis(day.5, CanadianWeather$dailyAv[,,"Temperature.C"], daybasis65,
                                  fdnames=list("Day", "Station", "Deg C"))$fd

  res.pca<-mfpca(daytempfd)
  plot.mfpca(res.pca)


  ####Multivariate case: "Canadian temperature" data (Ramsey & Silverman)
  daybasis65 <- create.fourier.basis(c(0, 365), nbasis=65, period=365)
  daytempfd <- smooth.basis(day.5, CanadianWeather$dailyAv[,,"Temperature.C"], daybasis65,
                                  fdnames=list("Day", "Station", "Deg C"))$fd
  dayprecfd<-smooth.basis(day.5, CanadianWeather$dailyAv[,,"Precipitation.mm"], daybasis65,
                                fdnames=list("Day", "Station", "Mm"))$fd

  res.pca<-mfpca(list(daytempfd,dayprecfd))
  plot.mfpca(res.pca)

funHDDC documentation built on May 9, 2026, 1:06 a.m.