Calculate a multivariate functional principal component analysis
for data observed on different dimensional domains. The estimation algorithm
relies on univariate basis expansions for each element of the multivariate
functional data (Happ & Greven, 2018)
MFPCA is an
R-package for calculating a PCA for multivariate functional data observed on different domains, that may also differ in dimension. The estimation algorithm relies on univariate basis expansions for each element of the multivariate functional data.
MFPCA allows to calculate a principal component analysis for multivariate (i.e. combined) functional data on up to three-dimensional domains:
It implements various univariate bases:
The representation of the data is based on the object-oriented
funData package, hence all functionalities for plotting, arithmetics etc. included therein may be used.
If you would like to use the cosine bases make sure that the
fftw3 is installed on your computer before you install
MFPCA is installed without the cosine bases and will throw an error if you attempt to use functions that need
The theoretical foundations of multivariate functional principal component analysis are described in:
C. Happ, S. Greven (2016+): Multivariate Functional Principal Component Analysis for Data Observed on Different (Dimensional) Domains. Journal of the American Statistical Association, to appear. Accepted author version, ArXiv link.
Please use GitHub issues for reporting bugs or issues.
MFPCAmain function enables stratified bootstrap. Bootstrap CIs for eigenvalues are returned, too.
MFPCAfunction now returns the eigenvectors and normFactors that are calculated within the MFPCA calculation. They can be used to calculate out-of-sample predictions.
fcptpaBasisnow returns eigenvalues and has new option to normalize the eigenfunctions.