mfpca {Funclustering} | R Documentation |
This function runs a weighted functional pca in the univariate 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 not given, a classic functional pca (without weighting the curves) is performed. |
With univariate functional data, the function returns an object of class pca.fd
,
With multivariate data, a list of pca.fd
object is returned. 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)