mfpca {Funclustering} | R Documentation |
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.
mfpca(fd, nharm, tik = numeric(0))
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). |
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
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)