mfpca: Multivariate functional pca

In Funclustering: Functional Data Clustering

Description

This function will run a weighted functional pca in the two cases of uni, and multivariate cases. If the observations (the curves) are given with weights, set up the parameter tik.

Usage

mfpca(fd, nharm, tik = numeric(0))

Arguments

fd	in the univariate case fd is an object from a class fd. Otherwise in the multivariate case fd is a list of fd object (fd=list(fd1,fd2,..)).
nharm	number of harmonics or principal component to be retain.
tik	the weights of the functional pca which corresponds to the weights of the curves. If don't given, then we will run a classic functional pca (without weighting the curves).

Value

When univarite functional data, the function are returning an object of class pca.fd, When multivariate a list of pca.fd object by dimension. The pca.fd class contains the folowing parameters:

• harmonics: functional data object storing the eigen function

• values: the eigenvalues

• varprop: the normalized eigenvalues (eigenvalues divide by their sum)

• scores: the scores matrix

• meanfd: the mean of the functional data object

Examples

data(growth)
data=cbind(matrix(growth$hgtm,31,39),matrix(growth$hgtf,31,54));
t=growth$age;
splines <- create.bspline.basis(rangeval=c(1, max(t)), nbasis = 20,norder=4);
fd <- Data2fd(data, argvals=t, basisobj=splines);
pca=mfpca(fd,nharm=2)
summary(pca)

Funclustering documentation built on May 2, 2019, 5:05 p.m.